{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d1234188",
   "metadata": {},
   "source": [
    "# Session 2: Classification and Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e105858",
   "metadata": {},
   "source": [
    "## Getting started"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c518974",
   "metadata": {},
   "source": [
    "First, we will load the packages we need for these exercises."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8916170d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:45:32.562065Z",
     "start_time": "2022-08-23T13:45:21.229624Z"
    }
   },
   "outputs": [],
   "source": [
    "# This line is needed to force matplotlib to display inline in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# The 3 lines below here suppress ConvergenceWarnings -- this is not necessarily something\n",
    "# I would recommend doing in practice, but avoids spammed warnings in a particular cell\n",
    "# towards the end of this file\n",
    "from warnings import simplefilter\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "simplefilter(\"ignore\", category=ConvergenceWarning)\n",
    "\n",
    "import os\n",
    "import pickle\n",
    "import tempfile\n",
    "\n",
    "import polars as pl                        # Work with data frames\n",
    "import numpy as np                         # Simple mathematics function and linear algebra\n",
    "import matplotlib.pyplot as plt            # Charting functions\n",
    "plt.rcParams['figure.figsize'] = [12, 8]   # Make plots larger by default\n",
    "from sklearn import cluster                # For using kmeans\n",
    "from sklearn import linear_model           # GLM-type models -- both econometric and ML\n",
    "from sklearn import model_selection        # Training and testing splits\n",
    "from sklearn import metrics                # Model performance evaluation\n",
    "from sklearn import neighbors              # For KNN\n",
    "from sklearn import preprocessing          # For standardizing data for LASSO and Elastic net\n",
    "from sklearn import svm                    # For SVM-type models\n",
    "import xgboost as xgb                      # For xgboost models\n",
    "\n",
    "# Numba cannot cache beside packages installed on some mapped/network drives.\n",
    "# Set a writable cache location before importing UMAP.\n",
    "os.environ.setdefault('NUMBA_CACHE_DIR', os.path.join(tempfile.gettempdir(), 'numba_cache'))\n",
    "import umap.umap_ as umap                  # Needed for the custom function `umap_compare_svm()`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fe4f84a",
   "metadata": {},
   "source": [
    "Next, we need to import the dataset for the sessions exercises.\n",
    "\n",
    "We will also split the data file like we did for Session 1 and apply the needed transformations to it using `sklearn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "846654bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:45:53.973254Z",
     "start_time": "2022-08-23T13:45:53.520573Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load data\n",
    "# Scan the full file when inferring types because some numeric columns begin with integers\n",
    "# but contain floating-point values later in the file.\n",
    "df = pl.read_parquet('../../Data/S1_data.parquet')\n",
    "\n",
    "# Define variable sets\n",
    "vars_financial = ['logtotasset', 'rsst_acc', 'chg_recv', 'chg_inv', 'soft_assets', 'pct_chg_cashsales', 'chg_roa',\n",
    "                  'issuance', 'oplease_dum', 'book_mkt', 'lag_sdvol', 'merger', 'bigNaudit', 'midNaudit', 'cffin',\n",
    "                  'exfin', 'restruct']\n",
    "vars_style = ['bullets', 'headerlen', 'newlines', 'alltags', 'processedsize', 'sentlen_u', 'wordlen_s', 'paralen_s',\n",
    "              'repetitious_p', 'sentlen_s', 'typetoken', 'clindex', 'fog', 'active_p', 'passive_p', 'lm_negative_p',\n",
    "              'lm_positive_p', 'allcaps', 'exclamationpoints', 'questionmarks']\n",
    "vars_topic = ['Topic_' + str(i+1) + '_n_oI' for i in range(0,31)]\n",
    "\n",
    "# Subset the final year to be the testing year\n",
    "train = df.filter(pl.col('year') < 2004)\n",
    "test = df.filter(pl.col('year') == 2004)\n",
    "\n",
    "# Set up the data for the linear problem\n",
    "vars_linear = vars_topic\n",
    "scaler_X = preprocessing.StandardScaler()\n",
    "scaler_X.fit(train.select(vars_linear).to_numpy())\n",
    "train_X_linear = scaler_X.transform(train.select(vars_linear).to_numpy())\n",
    "test_X_linear = scaler_X.transform(test.select(vars_linear).to_numpy())\n",
    "\n",
    "scaler_Y = preprocessing.StandardScaler()\n",
    "train_sdvol = train.get_column('sdvol1').to_numpy().reshape(-1, 1)\n",
    "test_sdvol = test.get_column('sdvol1').to_numpy().reshape(-1, 1)\n",
    "scaler_Y.fit(train_sdvol)\n",
    "train_Y_linear = scaler_Y.transform(train_sdvol)\n",
    "test_Y_linear = scaler_Y.transform(test_sdvol)\n",
    "\n",
    "# Set up the data for the binary classification problem\n",
    "vars_logistic = vars_topic + vars_financial + vars_style\n",
    "scaler_X = preprocessing.StandardScaler()\n",
    "scaler_X.fit(train.select(vars_logistic).to_numpy())\n",
    "train_X_logistic = scaler_X.transform(train.select(vars_logistic).to_numpy())\n",
    "test_X_logistic = scaler_X.transform(test.select(vars_logistic).to_numpy())\n",
    "\n",
    "train_Y_logistic = train.get_column('Restate_Int').to_numpy()\n",
    "test_Y_logistic = test.get_column('Restate_Int').to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90d08c1",
   "metadata": {},
   "source": [
    "Below I have also defined some custom functions.  Don't worry about these until we get to using them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9db48556",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:46:10.074891Z",
     "start_time": "2022-08-23T13:46:09.954377Z"
    }
   },
   "outputs": [],
   "source": [
    "# From umap.plot source code on Github\n",
    "def _get_embedding(umap_object):\n",
    "    if hasattr(umap_object, \"embedding_\"):\n",
    "        return umap_object.embedding_\n",
    "    elif hasattr(umap_object, \"embedding\"):\n",
    "        return umap_object.embedding\n",
    "    else:\n",
    "        raise ValueError(\"Could not find embedding attribute of umap_object\")\n",
    "\n",
    "\n",
    "def umap_color(data_map, data_color, cmap='viridis', subset=None, title=None):\n",
    "    \"\"\"Plot a UMAP embedding colored by numeric or categorical labels.\"\"\"\n",
    "    data_map = np.asarray(data_map)\n",
    "    data_color = np.asarray(data_color)\n",
    "    embed = _get_embedding(umap.UMAP(random_state=42, n_jobs=1).fit(data_map))\n",
    "\n",
    "    if subset is not None:\n",
    "        embed = embed[subset]\n",
    "        data_color = data_color[subset]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
    "    point_size = 100.0 / np.sqrt(len(embed))\n",
    "    if np.issubdtype(data_color.dtype, np.number):\n",
    "        points = ax.scatter(embed[:, 0], embed[:, 1], s=point_size, c=data_color, cmap=cmap)\n",
    "        fig.colorbar(points, ax=ax)\n",
    "    else:\n",
    "        categories = np.unique(data_color)\n",
    "        colors = plt.get_cmap(cmap)(np.linspace(0, 1, len(categories)))\n",
    "        for category, color in zip(categories, colors):\n",
    "            mask = data_color == category\n",
    "            ax.scatter(embed[mask, 0], embed[mask, 1], s=point_size, color=color, label=category)\n",
    "        ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "    ax.set(xticks=[], yticks=[], title=title)\n",
    "    return ax\n",
    "\n",
    "\n",
    "# Cut down version of umap.plot.points to remove dependencies on  datashader, bokeh, holoviews, scikit-image, and colorcet\n",
    "def umap_compare_svm(X, Yhat, Y, clip = None, cmap='viridis', subset=None, binary=False, title=None):\n",
    "    reducer = umap.UMAP()\n",
    "    umap_object = reducer.fit(X)\n",
    "    embed = _get_embedding(umap_object)\n",
    "    if clip is not None:\n",
    "        Yhat = np.clip(Yhat, clip[0][0], clip[0][1])\n",
    "        Y = np.clip(Y, clip[1][0], clip[1][1])\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "    if subset is not None:\n",
    "        embed_X = embed[subset,0]\n",
    "        embed_Y = embed[subset,1]\n",
    "        Y = np.array(Y[subset])\n",
    "        X = np.array(X[subset])\n",
    "        Yhat = np.array(Yhat[subset])\n",
    "    else:\n",
    "        embed_X = embed[:, 0]\n",
    "        embed_Y = embed[:, 1]\n",
    "    \n",
    "    point_size = 100.0 / np.sqrt(len(embed_X))\n",
    "    \n",
    "    if binary:\n",
    "        point_size = point_size * (1 + Y * binary)\n",
    "    \n",
    "    # color by values for Yhat\n",
    "    points = ax1.scatter(embed_X, embed_Y, s=point_size, c=Yhat, cmap=cmap)\n",
    "    fig.colorbar(points, ax=ax1, orientation='horizontal')\n",
    "\n",
    "    ax1.set(xticks=[], yticks=[])\n",
    "    ax1.set_title(\"Predicted values\")\n",
    "    \n",
    "    # color by values for Y\n",
    "    points = ax2.scatter(embed_X, embed_Y, s=point_size, c=Y, cmap=cmap)\n",
    "    \n",
    "    fig.colorbar(points, ax=ax2, orientation='horizontal')\n",
    "\n",
    "    ax2.set(xticks=[], yticks=[])\n",
    "    ax2.set_title(\"Actual values\")\n",
    "    \n",
    "    if title is not None:\n",
    "        fig.suptitle(title)\n",
    "    \n",
    "    if clip is not None:\n",
    "        foot = 'Predicted values winsorized to [{}, {}]; Actual values winsorized to [{}, {}]'.format(clip[0][0], clip[0][1], clip[1][0], clip[1][1])\n",
    "        plt.figtext(0.2, 0.3, foot, horizontalalignment='left')\n",
    "    \n",
    "    return (ax1, ax2)\n",
    "\n",
    "\n",
    "def logistic(x):\n",
    "    return 1 / (1 + np.exp(-1 * x))\n",
    "\n",
    "\n",
    "def coefplot(names, coef, title=None):\n",
    "    # Make sure coef is list, cast to list if needed.\n",
    "    if isinstance(coef, np.ndarray):\n",
    "        if len(coef.shape) > 1:\n",
    "            coef = list(coef[0])\n",
    "        else:\n",
    "            coef = list(coef)\n",
    "    \n",
    "    # Drop unneeded vars\n",
    "    data = []\n",
    "    for i in range(0, len(coef)):\n",
    "        if coef[i] != 0:\n",
    "            data.append([names[i], coef[i]])\n",
    "    data.sort(key=lambda x: x[1])\n",
    "    \n",
    "    # Add in a key for the plot axis\n",
    "    data = [data[i] + [i+1] for i in range(0,len(data))]\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(4,0.25*len(data)))\n",
    "\n",
    "    ax.scatter([i[1] for i in data], [i[2] for i in data])\n",
    "    \n",
    "    ax.grid(axis='y')\n",
    "    ax.set(xlabel=\"Fitted value\", ylabel=\"Residual\", title=(title if title is not None else \"Coefficient Plot\"))\n",
    "    \n",
    "    ax.axvline(x=0, linestyle='dotted')\n",
    "    ax.set_yticks([i[2] for i in data])\n",
    "    ax.set_yticklabels([i[0] for i in data])\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79654a5",
   "metadata": {},
   "source": [
    "## SVM and SVR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fab5cab",
   "metadata": {},
   "source": [
    "In computer science and many other discplines, a simple type of classifier used in training models is SVM.  This is particularly common for algorithms that are being used as supervised methods for predicting some variable in a model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdfc8012",
   "metadata": {},
   "source": [
    "### SVM: Support Vector Machine for Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b6b2ccd",
   "metadata": {},
   "source": [
    "This is the most common approach for these algorithms.  The algorithm will allow us to approach a classification problem, and it can either report the most likely class for each observation, or, like with logistic regression, it can report the probability of belonging to a given class.\n",
    "\n",
    "Multiple implementations are available in sklearn for this algorithm:\n",
    "\n",
    "- `sklearn.svm.LinearSVC()`: Fast and memory efficient; assumes a linear kernel.  Does not output probabilities!\n",
    "- `sklearn.svm.SVC()`: More flexible as it allows other kernel functions, but less efficient when using a linear kernel\n",
    "- `sklearn.linear.SGDClassifier()`: Can emulate `sklearn.svm.LinearSVC()` depending on specified parameters, allows some additional flexibility in parameters, is more memory efficient, and can do online (batch) training (good for very large data sets)\n",
    "\n",
    "Since we are using a **linear** kernel in this example, we can also make sense of the coefficients assigned to each input in our model.  We can actually use the exact same `coefplot()` as in Session 1 to visualize the algorithm.  However, if we change the kernel, such as using the **radial basis function** kernel, then we cannot use `coefplot()` meaningfully.  There are other ways to visualize this, however, as we will see later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7eabf450",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:46:14.079921Z",
     "start_time": "2022-08-23T13:46:13.925921Z"
    }
   },
   "outputs": [
    {
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       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC(C=1, dual=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual;\">\n",
       "            dual: &quot;auto&quot; or bool, default=&quot;auto&quot;<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples &gt; n_features.<br>`dual=&quot;auto&quot;` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` &lt; `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br>   The `&quot;auto&quot;` option is added in version 1.3 and will be the default<br>   in version 1.5.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-penalty;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-penalty;\">\n",
       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;}, default=&#x27;l2&#x27;<br><br>Specifies the norm used in the penalization. The &#x27;l2&#x27;<br>penalty is the standard used in SVC. The &#x27;l1&#x27; leads to ``coef_``<br>vectors that are sparse.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('loss',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-loss;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
       "            loss\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-loss;\">\n",
       "            loss: {&#x27;hinge&#x27;, &#x27;squared_hinge&#x27;}, default=&#x27;squared_hinge&#x27;<br><br>Specifies the loss function. &#x27;hinge&#x27; is the standard SVM loss<br>(used e.g. by the SVC class) while &#x27;squared_hinge&#x27; is the<br>square of the hinge loss. The combination of ``penalty=&#x27;l1&#x27;``<br>and ``loss=&#x27;hinge&#x27;`` is not supported.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;squared_hinge&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('multi_class',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-multi_class;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
       "            multi_class\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-multi_class;\">\n",
       "            multi_class: {&#x27;ovr&#x27;, &#x27;crammer_singer&#x27;}, default=&#x27;ovr&#x27;<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``&quot;ovr&quot;`` trains n_classes one-vs-rest classifiers, while<br>``&quot;crammer_singer&quot;`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``&quot;crammer_singer&quot;`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
       "            intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a &quot;synthetic&quot; feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1000</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes,)<br><br>The unique classes labels.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=coef_,-ndarray%20of%20shape%20%281%2C%20n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            coef_: ndarray of shape (1, n_features) if n_classes == 2             else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>``coef_`` is a readonly property derived from ``raw_coef_`` that<br>follows the internal memory layout of liblinear.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1, 68)</td>\n",
       "           <td>[[ 0.01,-0.  ,-0.  ,...,-0.  ,-0.01,-0.2 ]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%2C%29\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)<br><br>Constants in decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
       "           <td>[-1.]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>68</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_iter_,-int\">\n",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>10</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
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       "    }\n",
       "\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "/**\n",
       " * Copy the list of feature names formatted as a Python list.\n",
       " *\n",
       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
       " *   contain a `details` element and a table containing feature named.\n",
       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
       " */\n",
       "function copyFeatureNamesToClipboard(element) {\n",
       "    var detailsElem = element.closest('.features').querySelector('details');\n",
       "    var wasOpen = detailsElem.open;\n",
       "    detailsElem.open = true;\n",
       "    var content = element.closest('.features').querySelector('tbody')\n",
       "                  .innerText.trim();\n",
       "    if (!wasOpen) detailsElem.open = false;\n",
       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
       "    const originalStyle = element.style;\n",
       "    const copyMark = document.createElement('span');\n",
       "    copyMark.innerHTML = '✔';\n",
       "    copyMark.style.color = 'blue';\n",
       "    copyMark.style.fontSize = '1em';\n",
       "\n",
       "    navigator.clipboard.writeText(formattedText)\n",
       "        .then(() => {\n",
       "            element.style.display = 'none';\n",
       "            element.parentElement.appendChild(copyMark);\n",
       "\n",
       "            setTimeout(() => {\n",
       "                copyMark.remove();\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'orange';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-1');</script></body>"
      ],
      "text/plain": [
       "LinearSVC(C=1, dual=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svc = svm.LinearSVC(C=1, dual=False)\n",
    "model_svc.fit(train_X_logistic, train_Y_logistic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2a77613",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:46:15.595056Z",
     "start_time": "2022-08-23T13:46:15.470564Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x18893b823c0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+czNSy5QpU+AJAAAAKElFSqDnnHOOXXTRRdatW7cCb5OWlhaNdgEAkqT+a2GoDQugxAPtjTfeaA899FChgTYzMzMa7QIAxBD1XwGkbKA95ZRT7OCDDy70NlQ4AIDEF4/6r4WhNiyAEgu0Gk7QsGHDYj0hACA1678WhtqwAIqDWVwAkOKo/wrA76JehxYAAAAoSQRaAAAA+BqBFgAAAL5GoAUAAEBqTgrLyspyP3v27FnoNgBA4i2iwIIGAJJJWkB/5SK54/+vCBZ69/y2JaLNmze7BSA2bdpkVapUiXdzACCuiyjMGdjJMtIpegPAv/ks4r9g27dvD2sbACBxF1FgQQMAySDiQFu+fPmwtgEAEncRBRY0AJAMOMYEACmERRQApHSgrVmzZtgPunr16kjbAwAAAMQm0D7++ONFe2QAAAAgkQJt9+7dY9sSAAAAoKQXVlBZhRkzZhTnIQAAAICSD7QKsl27drWqVataq1atgtvPP/98mzJlSvFaBAAocp3ZbTm7Cjn9b0EFAEhGEVU5uPfee13x219++cVatGgR3H799dfbwIED7bPPPotmGwEAES6aAACpIKJAO3LkSMvOzrb69evn2n7ccce5nlsAQPwXTciLRRQAJKuIAu369eutevXquZa7lT///DPXZQBA/BZNyItFFAAkq4jG0B599NGul1ZCA+xjjz1mbdq0iV7rAABFXjShoBMdDgCSVUQ9tA899JB16dLFvvvuOzd+a8CAATZmzBibPn26ffPNN9FvJQAAABDNHtqTTz7ZBdcNGzbYwQcfbP/617+sVq1aNnHiRHpoAQAAkPg9tN6wg/feey+6rQEAAABKKtCKKh3MnTvXnW/evDm9swBQwgIB3nIAiCjQLl261C688EK3iML+++/vtq1bt87atm1r77//vtWpU4d3FgBiTHMYumVl8z4DSHkRjaG97rrrrFKlSrZo0SJbu3atO+l8RkaG9ejRI+XfVAAoqRq0c1Ztdueb16riynIBQCqKqId2woQJNn/+fKtbt25wmyaHvfHGG3bIIYdEs30AgDB80LMtZbkApKyIemhr165d4HUHHXRQcdoDAIgAa9oASGURBdprr73WDS1YsmRJcJvOa7tOAAAAQMINOWjcuHHw/J49e2zx4sVumMEBBxzgJiZoHK0sXLjQevfuHZvWAgAAAJEG2rvuuivcmwIAAACJF2h79uwZ25YAAAAAJTWGFgAQXxrqtS1nN7sBAIqzUthPP/1kH374oVtkYdeuXbmuGzp0KG8uAMQwzHbNyrZpSzbwHgNApD20I0aMcKuCzZw50958800XaLVq2DvvvGNbt27ljQWAGC+oEBpmW9evxqIKAFJaRD20AwcOtLfeesu6devmCnkPGzbMdu/ebbfddptt2bIl+q0EAORrat+OVr1iOosqAEhpEQXaefPmWefOnf/7AGXK2LZt29yyt/369bNmzZpFu40AgAJkpJcmzAJIeRENOdixY4cLsN6qYQq4kpOT464DAAAAEn5SmOfcc8+1q6++2i6++GL7+OOPrV27dtFpGQAAABCrHtqxY8cGzw8ePNjat2/vwmzTpk1tyJAhkTwkAAAAUHI9tB07dgye19CDp556KrJnBwAUWJpL1QzyQ/1ZAIjykAMAQHRRZxYAYhRoa9asGfaDrl69uojNAAAUVGe2INSfBYAiBtrHH3883JsCAKJYZ1alufJToSwluwCgSIG2e/fuvGMAUMIUZjPSGR0GAFGvcgAAAAAkirh/7f/tt9/sjTfesDVr1ljLli3t2muvtfLly+9zwsSnn35qX3/9tauycMUVV7iSYQAAAEg9ce2hnT17th1xxBE2Z84ca9KkiWVlZbmFGXbu3FngfXTdOeecYzfffLPVqFHDnS6//HKbNWtWibYdAAAAiSGuPbT33HOPHXvssfbBBx8Ex+nWr1/f3nrrLddTm58nn3zSvv32W/vll1+sTp06blvPnj3tzz//LNG2AwAAIAkC7aZNm2zRokV21FFHFfm+OTk5bsWxF198MbhNva2nnHKKG05QUKB9+eWXXY+sF2alXLly7gQgtRYYSFYsnAAAJRBoFWQVOEeMGBH8hyPnn3++63U97rjj9vkYS5cudcMHGjRokGu7Ln/33XcFPu/ixYvd42uJ3WnTplnt2rXtkksuscaNGxf4XDt27HAnz+bNm8N+rQDihwUGAAAxG0N77733ulCow/6hrr/+ehs4cGBYj7F9+3b3s2LFirm2V65cOXhdXlu2bHE/77//fjchTJPIFixYYC1atLDx48cX+FyDBw+2zMzM4Klu3bphtRGAPxYYSFYsnAAAMeyhHTlypGVnZ7vxrqHUc9q1a9ewHqNKlSru58aNG3NtX79+vQud+fG2H3roofbvf//bnb/ppptc0O3fv7+1b98+3/v17t3bevXqFbysME6oBZJngYFkxcIJABDDQKvQWb16dXc+LS0tuF0Ts0IvF0aBUgFVvbxnnHFGrsoHhx12WL73Ue+tQnTe69VD+8477xT4XIyxBfyPBQYAAFEdcnD00Ue7XloJDbCPPfaYtWnTJqzHKFWqlF100UX2+uuv29atW922KVOmuNOll14avJ1q1Pbt2zd4WRPCxowZExwTq3G4o0ePtmOOOSaSlwIAAIBU7KF96KGHrEuXLm7yliZtDBgwwIXM6dOn2zfffBP242hsa8eOHd1YWJ1031tuucU6deoUvM3EiRNt8uTJNmjQoODwAV1u1qyZtW7d2j2nFldQOS8AAACknrSAV6KgiFRh4NFHH7WpU6fanj17rFWrVtanTx/Xe1sUu3btsgkTJgRXCss7nGDSpEm2du1at5iCR01WqF2yZInVq1fPjd0tXTr8sXUaQ6vhDqqa4I3lBZB4tuXssub9xrjzcwZ2soz0uC9uCACIkeLks4gCrZ7Qz0GQQAv4A4EWAFLH5mIE2ojG0GoBBI1/1QIIhS1TCwCR0PdshVkWGAAAxCzQqqKAhgp069bNatWqZX//+99dGS8AiNZiChpq0HrQV7yhAIDYBFqtCKZVwjTu9ZFHHrG5c+faCSec4FbrUj1YAIjmYgosMAAAiMmksLxUT/ayyy6zn3/+ObgUbqJiDC3gj3Gz3mIKLDAAAMlvc0mPofWoFuyHH35oF1xwgatusGrVKld2CwCiuZhCuAu2AABSU0Q1cMaPH+/G0Q4fPtyNpVVJrY8++shOPfVUK1OGsjoAAAAoORGlz9NOO80tiPD888/beeedZxUrVox+ywAAAIBYBdoVK1bYgQceGMldAQAAgPgHWsIsgHBogqiqFhQFtWcBADELtDVr1nQ/V69eHTxfEN0GQGrz6snmLcEFAEDcAu3jjz+e73kACLeebFFQexYAEPVA27179+D53377zfr27Zvv7QYNGhT2kwNIDV492aKg9iwAIKYLK6gmZEF3K+y6RMHCCkDJLpAwZ2AnV08WAICEW1ghr3nz5tn+++8fzYcEAAAAClWkLpM6derke1727Nlja9eutR49ehTlIQEAAICSC7Te+Nirr756r7GyZcuWtQYNGtjxxx9fvBYBAAAAsQq0V111lfupYQVdunQpyl0BAACAmIholgZhFsC+FlBggQQAQElhYQUAxcICCgCAeGNhBQAxXUCBBRIAAAm5sELoeQAobAEFFkgAAMRaRHVo16xZY88880zw8ksvveQqHHTo0MFWrFgRzfYB8BGFWS2gEHrSYisAACRcoL3zzjutRo0a7vzKlSutV69eduONN1rlypXddQAAAEBCVzkYM2aMPf/88+786NGj7dRTT7V77rnHVq1aZUcccUS02wgAAABEt4d2165d9tdff7nzY8eOdUMNpEKFCpaTkxPJQwIAAAAl10N74okn2g033GAnn3yyjRw50h588EG3ffLkyfa3v/0tspYAAAAAJdVD+8ILL1ipUqVs6NCh9uyzz1rDhg3d9ldeecX69esXyUMC8GH92W05u1hAAQDgzx7aunXr2kcffbTX9g8//DAabQKQ4FhMAQDg+x7aUFu2bLHNmzdHpzUAfLuYAgsoAAB8FWjVO/Pcc89ZnTp1rEqVKpaZmenOa5uuA5BaiynMGdjJPujZlpqzAAD/DDkYPHiwPfbYY67mbJs2bdw/sezsbDd+Vj22ffr0iX5LAST0YgoAAMRLRP+FsrKy7L333rPTTjstuE2lu4455hi77rrrCLQAAABI7CEHq1evdj2zeWmblsUFAAAAEjrQHnLIIa6HNq9hw4ZZkyZNotEuAAAAIHZDDu6//367+OKL7bPPPrNjjz3WbZsyZYq7rFALIDlokqcqGuS1LWfvbQAA+CrQdu3a1SZOnOgmhr399ttuUljz5s3dtuOOOy76rQRQ4qg1CwBI2kD78ssv2yeffOL+2Z1zzjk2fPjw2LQMQMLVms2L2rMAAN8F2ieffNLuvvtua9eunbt800032bZt2+yOO+6IVfsAJEitWZXnyqtC2dLUngUA+GtS2KuvvmpDhw61cePGudObb75pr7zySuxaByChas3mPWm4EQAAvgq0ixcvtvPOOy94+YILLnDbAAAAAF8E2h07dlj58uWDlytUqOC2AQAAAL6ZFHbzzTfvc9vzzz9fvFYBAAAAsQi0Rx99tE2ePHmf2wAAAICEDLRTp06NXUsAJNQCCiyeAABIukCrsbLlypWL+m0BxBcLKAAAUmZSWNOmTe21115zdWcLsnXrVlfaS7cFkBwLKLB4AgAgaXpo//3vf9vtt99uvXr1sg4dOrixszVq1HC9O6tXr7Yff/zRvv76a2vRooW9++67sW01gBJbQIHFEwAASRNo27Zta1OmTLEJEybYsGHD7P3337dly5a5wup16tSxE044wUaPHu1+AvD3AgoAAPhJkf9znXTSSe4EAAAA+G5hBQAAACDREGgBAADgawyWA+Jc7zXeqDcLAPA7Ai1QQqj3CgBAbDDkAEiQeq/xRr1ZAEDK9dD+9NNP9uabb9qiRYts5MiRbtvQoUPt/PPPt4yMjGi2EUiJeq/xRr1ZAEBKBVrVm1Vw7dKli33yySfB7QsWLLCnn37a+vTpE802AkmHeq8AAMR5yEHfvn3tjTfecIsrhLrkkktsyJAh0WobAAAAEJtAO2fOHDvrrLPcea0U5jnooINs+fLlkTwkAAAAUHKBNjMzMxhcQwPt5MmTXagFAAAAEjrQamjBrbfeaqtWrXKXd+7caWPGjLEePXrYZZddFu02AgAAANENtA8++KBVrFjR9cbu2bPHKlWqZKeffrq1bt3a/vnPf0bykEBS1p3dlrMr5JRYCyoAAJDSVQ4qVKhgI0aMsLlz59rUqVNdqG3VqpW1bNky+i0EfIhFFAAASPBAW6ZMGdu1a5c1a9bMnfK7DkhlhS2iwAIGAAAkQKDdvTv/Q6c5OTku0AIoeBEFFjAAACC6ipQ+Q2vM5q03q2EHP/zwgx166KHRax2QBFhEAQCABAq0gwYNyve8lC1b1ho0aGBZWVnRax0AAAAQzUD722+/uZ+qZqDJYAAAAIAvy3YRZgEAAJAoijWDSwsqrFixYq+qBo0bNy5uuwAAAIDYBdp169bZDTfcYCNHjsy34oFqcAKpRp97lesSFlEAACDBA22vXr1s+/btbujBUUcd5RZYUIWD3r1721133RX9VgIJjoUUAADwWaD98ssvLTs72w4++GB3uUmTJta0aVOrV6+e3XrrrXbHHXdEu52ALxdSYBEFAAASNNCuWbPGleiSzMxM++OPP+zAAw+0Y4891ubNmxftNgK+XUiBRRQAAEjQKgeSlpbmfh522GE2dOhQd3748OFWq1at6LUO8PFCCjp5vycAACDBemiPP/744Pn777/fzj77bLvvvvtc1YNXXnklmu0DAAAAoh9ov//+++D5jh072sKFC+3nn392Y2kbNWoUyUMCAAAAJV+H1qNhBt5Qg2nTptnRRx8djYcFAAAAYjOG9q+//rItW7bk2rZo0SK75JJL7JhjjonkIYGEKsG1LWdXEU9712MGAAAJ2EO7evVqu+KKK2zcuHG2Z88e69y5s7377rv20ksvWb9+/ezQQw+1UaNGxa61QIxRTxYAgCQPtPfee68tXbrUnnnmGXf52WeftQ4dOtiCBQvsxRdftKuuuspKlYq4cAKQsPVkw0XdWQAAEjzQjh071kaPHm0tW7Z0l0888UQ78sgj7auvvnLBFkjWerLhou4sAAA+GHLQokWL4GUv2J588snRbxmQIPVkAQBAYivS+ACNmw0dUuCdL1OGf/oAAACIjyIn0Tp16uxz2/Lly4vXKgAAACAWgfaBBx4oys0BAACAxAq0ffv2jV1LAAAAgAgw+BX4//qzKtnFAgkAAPgPgRYpj8UUAADwN1ZBQMrLbzEFFkgAAMA/6KEF8llMgQUSAABIoR5a1aYFkm0xhbS0tHg3BQAAxDLQ7tq1ywYNGmQHH3xwrkUVbrnlFlu0aBFvPgAAABI70D788MM2dOhQe/DBB92EGk/btm1twIAB0WwfAAAAEP1A+/rrr9t7771nl156aa7t7du3t1GjRkXykAAAAEDJBdoVK1bYIYcc4s6HjjUsW7as/fnnn5G1BAAAACipQNukSRPLzs7eK9D++9//tpYtW0bykECJ03CZbTm7WEwBAIBULNt1zz33WPfu3a1fv37u8siRI2306NE2ZMgQe/fdd6PdRiDqWEwBAIAUD7SXX365+6lKByrbde6551rDhg3d2NquXbtGu41A1LGYAgAAySPihRUUanXasmWLC7WZmZnRbRlQQlhMAQCAFBxD26JFCxs8eLAtXbrUKleuTJiFr7GYAgAAKRhoL7jgAnvttdesQYMGdvLJJ9urr75qGzdujH7rAAAAgFgE2oEDB9qCBQts0qRJdvjhh9t9991nNWvWdEH3o48+iuQhAQAAgJILtJ42bdrYc889ZytXrnRBduHChXb++ecX5yEBAACAkpkU5lGIVf3Zd955x+bPn28nnnhicR8SiLmQFZsBAEAq9tCuXbvWnn/+eWvbtq01btzYLYN75ZVX2uLFi23ChAnRbyUQ5Rq03bL+uzAIAABI0R7a2rVrW40aNeziiy+2l156yY488sjotwyIYQ3aOas2u/PNa1WxCmVL814DAJBqgXbMmDHWrl07K1WqWENwgbj7oGfbXMs3AwCAFAm0p5xySvRbAsQBWRYAgBQKtKeffrr7OXr06OD5gug2AAAAQEIF2sMOOyzf88Wl8KvSX2vWrLGWLVta//793YIN4dCCDs8884xbgveee+6JWpsAAACQhIH28ccfD57X+NkuXbrke7tRo0YVKcyeddZZ9sADD7iKCU8//bSdcMIJNnv2bKtatWqh9501a5a7n6xatSrs5wQAAEByiWhWl0JoJNflpd5YVUq499573RK6w4YNsz///NOysrIKvd+2bdvsoosucqXD9ttvvyK1HQAAAMklqmUK1q1bZ5mZmWHdduvWrfbjjz/amWeeGdxWrlw569ixo40fP77Q+95yyy0uAJ999tnFbjOSt9bstpxdBZx2x7t5AAAgXlUOQocZ5B1ysGfPHps7d27YK4WtWLHChY5atWrl2q7Lv/zyS4H3Uy/uxIkTbfr06WG3e8eOHe7k2bz5vzVIkZz0ueqalW3TlmyId1MAAECiBVqtCpbfeSlbtqx17tzZTdAKx65du9zP9PT0XNvVS7tz585877No0SK7+eabXR3cjIyMsNs9ePBgGzBgQNi3h/8XTggnzLauX41FFQAASLVAq0lbsv/++1vfvn2L9cTVq1d3P//4449c23VZj5+fzz//3LZv3+6W2fUsXLjQli9fbl999ZX9/PPPVrr03qs+9e7d23r16pWrh7Zu3brFaj/8YWrfjpaRnv9KYFohjEUVAABI0YUVihtmpWbNmm4J3SlTpuSaSJadnW0dOnTI9z6XXnqpq7AQqmvXrnbMMce4sl35hVmv11cnpB6F2Yz0iD7mAADAJ+K6sMINN9xgL774ol1zzTXWsGFDe+utt2z+/Pn27rvv5qqEMHPmTPvoo49cRYO8VQ3Kly/venujWRsXAAAA/hHXhRX69Oljv/32mzVt2tQNM1A5rtdff92OPPLIXJPHfv3116g8HwAAAJJPWkBTwuNsw4YNruRXvXr19hoasHLlShd0805CCx1DW7FiRTeEIVwaQ6vyYps2bbIqVaoUu/1ILCrN1bzfGHd+zsBODDkAAMAHipPPIqpDq4lZn376afDyuHHj7Nxzz7U777zTXVdU1apVsyZNmuQ7zlXjbAsKs9KoUaMihVkAAAAkl4gCbb9+/WzBggXuvFL0BRdcYBUqVLAvv/zSTc4C4ruYAgsnAACQSiKa/v3ee+/ZDz/84M6rJmzLli3dRC6NdW3fvr09++yz0W4nUCgWUwAAIHVF1EO7fv161yMrWqbWq3qg4QEbN26MbguBCBdTYOEEAABSQ0Q9tKpCMGjQIDvjjDPcUrQaQysqr3XEEUdEu41ARIspsHACAACpIaIe2qeeesqGDx9unTp1cqt2tWrVym1//PHH7a677op2G4GIFlNgFTAAAFJDRD20Wplr8eLFtnv37lyrc2mRhBo1akSzfQAAAED0e2g9eZeaJcwCAADAN4F2woQJ1qVLF1cHVsvW6ry2AQAAAAkfaFWi65RTTrHy5cvbjTfeaH//+9/deW3TdUD4NWOjdaL2LAAAqSqiMbQDBw60l19+2a699tpc24cMGeKuu+SSS6LVPiQhasYCAIC499AuXLjQunXrttd2bdN1QFFrxkYLtWcBAEg9EfXQ1qpVyyZNmhRcUMEzceJEt7gCUNSasdFC7VkAAFJPRIH25ptvdsMKbrvtNjv22GPdtilTptjTTz9tffv2jXYbkQI1YwEAACIVUZK4++67LTMz0x599FE3ZlZU6eCxxx6z66+/PuLGAAAAADEPtNnZ2TZq1Cg3seett94KrhKmKgcAAABAQgfaESNG2IUXXmjVqlVzlx955BH74IMP7Pzzz49V+wAAAIDoVTkYPHiwGyO7du1ad+rdu7c99NBDRXkIAAAAIH6Bdt68eXbnnXdaWlqaO2ksrbYBRREI8H4BAIA4BdqtW7dalSpVgpc1MUzbgHBp7HW3rGzeMAAAEL9JYVoNbF/bevToUbxWIakXVZizarM737xWFVc3FgAAoDjSAuoyC1OZMuHl3127dlki27x5s+td3rRpU64eZ8Tetpxd1rzfGHf+lwGdrGI5atACAAArVj4rUppI9KAKf0lLi3cLAABAyo2hBQAAABINgRYAAAC+RqAFAACArzEjB0WmeYSqVhCJbTmR3Q8AAKAgBFoUOcx2zcq2aUs28M4BAAB/DzlYv369vfnmm9a/f//gtqlTp9ru3fTAJTP1zEYjzLauX40atAAAIH49tDNnzrRTTz3V1Qr79ddfbcCAAW77yy+/bMcff7xdddVV0WkdEtrUvh0tIz2yhRG0oIKWTwYAAIhLD22vXr3slltusfnz5+fa/ve//92eeuqpYjcK/qAwm5FeJqITYRYAAMS1h/bHH3+0Dz/80J0PDSZNmjSx//znP1FrHAAAABCTHtpSpUrZn3/+udd29dhWq1YtkocEAAAASi7QnnnmmTZw4EDbs2dPsId22bJlduONN1qXLl0iawkAAABQUoH2iSeesG+//dbq1KnjQu1RRx1ljRs3tq1bt9rDDz8cyUMCAAAAJTeGtmbNmjZjxgw3jlaluhRqNVHswgsvtHLlykXWEgAAAKAkF1ZQcL3kkkvcCQAAAPBVoP3mm28Kvb5du3aRtgcAAACIfaBt3779PpdHBQAAABJ2UtjOnTtznXbs2GGzZ8+2E0880YYOHRr9VgIAAADRDLRlypTJdUpPT7cWLVrYa6+9Zo888kgkDwkAAACUXKAtSI0aNWzRokXRfEgAAAAg+mNoV69evde2DRs22KOPPmpNmzblLQcAAEBiB9patWrlu71hw4Y2bNiw4rYJAAAAiG2gnTVr1l7bqlWrZrVr1w4uhQv/UFWK7Tt3h3XbbTnh3Q4AACChA+3kyZOtR48e0W8N4hJmu2Zl27QlG3j3AQBA6kwKu+mmm2z3bnrqkoF6ZiMJs63rV7MKZUvHpE0AAAAx76Ft1qyZzZgxw1q3bh3J3ZGgpvbtaBnp4YVUhVmGlwAAAN8G2uuuu84uueQS69+/vzVv3tzVoQ112GGHRat9KEEKsxnpEX0kAAAA4qZI6WX06NF2+umn2y233OIuX3755fnejqVvAQAAkJCB9owzznBhddWqVbFrEQAAAFAEER1frlmzZiR3AwAAAKKOAZMpXmOWurIAACDlAu3FF1+8z9uwWlhioMYsAABIBUUOtOvWrYtNSxDXGrPUlQUAACkTaL/66qvYtARxrTFLXVkAAOBXjKFNEdSYBQAAySqipW8BAAAAXwbat99+O3YtAQAAAGIdaLt37x7JcwAAAAAxw5ADAAAA+BqTwpJokYS8WDQBAACkAgKtT7BIAgAAQP4YcpCEiyTkxaIJAAAgmdFDm4SLJOTFogkAACCZEWh9iEUSAAAA/ochBwAAAPA1Ai0AAAB8jUALAAAAXyPQAgAAwNeYFOaTxRRYJAEAACB/BNoExmIKAAAA+8aQA58tpsAiCQAAALnRQ+uzxRRYJAEAACA3Aq1PsJgCAABA/hhyAAAAAF8j0AIAAMDXCLQAAADwNQJtAgsE4t0CAACAxEegTeAatN2ysuPdDAAAgIRHoE3gGrRzVm1255vXquLKdQEAAGBvBFof+KBnW0tLS4t3MwAAABISgdYHyLIAAAAFI9ACAADA1wi0AAAA8DUCLQAAAHyNQAsAAABfKxPvBuB/dWdVqsuzLed/5wEAAFAwAm2ChNmuWdk2bcmGeDcFAADAdxhykADUM1tQmG1dvxqLKgAAABSCHtoEM7VvR8tI/9+qYFohjEUVAAAACkagTTAKsxnp7BYAAIBwMeQAAAAAvkagBQAAgK8RaAEAAOBrBNoEKNlFzVkAAIDIMfsojqg/CwAAUHz00CZQ/VlqzgIAABQdPbQJVH+2esV0as4CAAAUET20CVR/lgUUAAAAio5ACwAAAF8j0AIAAMDXCLQAAADwNQItAAAAfI0qB3GqP6uSXSyoAAAAUHwE2hLGYgoAAADRxZCDOC+mICyoAAAAEDl6aOO8mILqz1YoSw1aAACASBFo40hhNiOdXQAAAJAUQw527txZpNvv2bMnZm0BAACAf8Q90D7wwANWvXp1K1++vDVr1sy++uqrQm//2WefWfv27a1KlSpWqVIl69Spk82ePbvE2gsAAIDEEtdA+8ILL9hjjz1mI0aMsK1bt9qll15qZ511li1cuDDf2+/evdteeuklu//++23t2rW2bNky22+//ey0006zTZs2lXj7AQAAkOKB9umnn7Zrr73W2rVrZxUqVLB//vOfduCBB1pWVla+ty9durSNGjXKTj75ZHf7atWquUC8atUqmzJlSom3HwAAAPEXtxlJf/zxhy1YsMBOOumkXNsVVidPnhz246xYscL91LCFRF5EwcNiCgAAAEkSaNesWeN+HnDAAbm263K4va05OTl22223WZs2baxVq1YF3m7Hjh3u5Nm8ebOVBBZRAAAASIFJYXmrFehyWlraPu+n8bTdu3e3lStX2nvvvVfofQYPHmyZmZnBU926dS1eiyh4WEwBAADA5z20tWvXdj9///33XNt1uVatWvsMs1dccYVNmjTJvv32W6tXr16ht+/du7f16tUrVw9tSYXavIsoeFhMAQAAwOc9tFWrVrUWLVrYuHHjcvXOfv3113bCCScEt2mowPbt23Pd5sorr3RB9ptvvrFGjRrt87nKlSvnynyFnuK1iIJ3CqcXGgAAAAk+5ODee++1N954ww0ZWLp0qd16660uvN54443B2/z973+3Y445Jjgm9ZprrrGxY8e6agc1a9Z05b502rVrVxxfCQAAAOIlruuuagzstm3bbMCAAW6SWMuWLd3CCt5wBNGCCxkZGe78+vXrbfjw4e58aC+uV9NWPbcAAABILWkBdXumGI2h1eQwLcYQy+EH23J2WfN+Y9z5OQM7uaEGAAAAiG4+I2HFqN6sUHMWAAAg9gi0UUC9WQAAgBSuQ5sMCqs3K9ScBQAAiB16aGNcb1aoOQsAABA7BNoY1ZsFAABAyWDIAQAAAHyNQAsAAABfI9ACAADA1wi0AAAA8DUCLQAAAHyNQAsAAABfI9ACAADA1wi0AAAA8DUCLQAAAHyNQAsAAABfI9ACAADA1wi0AAAA8DUCLQAAAHyNQAsAAABfI9ACAADA1wi0AAAA8DUCLQAAAHyNQAsAAABfI9ACAADA1wi0AAAA8DUCLQAAAHytTLwb4GeBQMC279xt23J2x7spAAAAKYtAW4ww2zUr26Yt2RDdPQIAAIAiYchBhNQzmzfMtq5fzSqULR3pQwIAACAC9NBGwdS+HS0jvbQLs2lpadF4SAAAAISJQBsFCrMZ6byVAAAA8cCQAwAAAPgagRYAAAC+RqAFAACArxFoAQAA4GvMZCriIgoeFlMAAABIDATaMLCIAgAAQOJiyEGEiyh4WEwBAAAgvuihjXARBQ+LKQAAAMQXgbaIWEQBAAAgsTDkAAAAAL5GoAUAAICvEWgBAADgawTaMAQCsd8RAAAAiAyBNowatN2ysiN8ewEAABBrBNowatDOWbXZnW9eq4or0wUAAIDEQaAtgg96trW0tLTY7Q0AAAAUGYG2CMiyAAAAiYdACwAAAF8j0AIAAMDXCLQAAADwNQItAAAAfI1ACwAAAF8j0AIAAMDXCLQAAADwNQItAAAAfI1ACwAAAF8j0AIAAMDXCLQAAADwNQItAAAAfI1ACwAAAF8j0O5DIFAyOwIAAACRIdAWIhAIWLes7AjfWgAAAJQEAm0htu/cbXNWbXbnm9eqYhXKluZTCQAAkGAItGH6oGdbS0tLi+3eAAAAQJERaMNElgUAAEhMBFoAAAD4GoEWAAAAvkagBQAAgK8RaAEAAOBrBFoAAAD4GoEWAAAAvkagBQAAgK8RaAEAAOBrBFoAAAD4GoEWAAAAvkagBQAAgK8RaAEAAOBrBFoAAAD4GoEWAAAAvkagBQAAgK8RaAEAAOBrBFoAAAD4GoEWAAAAvkagBQAAgK8RaAEAAOBrBFoAAAD4Wpl4NyCRBQLxbgEAoCTt2bPHcnJyeNOBGChbtqyVLl06Fg9NoC1IIBCwblnZMXnTAQCJR0F28eLFLtQCiI2qVatazZo1LS0tLaqPSw9tAbbv3G1zVm1255vXqmIVysbmGwUAIDE6MVatWuV6j+rWrWulSjEiD4j279i2bdvs999/d5dr1aoV1ccn0Ibhg55to/5NAgCQOHbt2uX+2dauXdsyMjLi3RwgKVWoUMH9VKg98MADozr8gK+gYSDLAkBy2717t/uZnp4e76YASS3j/78w7ty5M6qPS6AFAOD/cTQO8OfvGIEWAIAkNX36dPv+++/j3YyENX78eFuyZEm8m5E0Jk6caL/++mtcnptACwCAT82aNcs+++yzAq9//fXX7fHHH7dEo0oS06ZNs08++cRmzJjhxjB7JkyYYN9++22B9/3oo49s9uzZe004mjlzpo0cOdK++eYbW7NmzT7b8J///Mcuvvhiq1Klyl7XDR8+3L7++usCQ1t+7dO40GHDhtnWrVuL3bZo+OWXX9x7pfc5XDt27HCvbcyYMbZ5838nxofavn27e/1ffPGFLVu2bK/rV69ebRdeeGFcKoUQaAtADVoAQKJ777337Lbbbivw+qOPPtpOPPFESyTz5s2zww47zM4//3x77bXX7Prrr3eXR40a5a7/4Ycf7JxzznHhKa+ff/7Z3e+PP/7IFYCbNWtmp59+unu8Bx54wFq3bm2XXnqp/fnnnwW2o2/fvtajRw+rVq1aru2TJ0+2bt262VlnnZVvqHvqqads8ODBe22fM2eOXXLJJS7UFbdtxREIBOzaa6+1v/3tb5aVleWe+4wzzrC//vqr0PuNGzfOGjRo4D5PL730kh177LG5evefe+45O/TQQ+0f//iHPfPMM+787bffnusxLrjgAje5Up/LkkaVg3xQgxYAkAyOOOIIFzA8U6ZMcf/jDj/8cNczqt7E4447ztUGza/3d+HCha6M2ZFHHplrRrrCm3odJTMz01q2bGl16tTJdX/vuVq0aGHZ2dluElDnzp3thhtusHr16rmeZe8xddhfYVWuuOIK69Onj+slvfzyy3M9pkJh48aN7aSTTnKX1ft42mmnuWA1aNAgK1Pmv7FGPYQffPCBC8UVK1bc67WtWLHCPv74Y5s7d+5e1w0ZMsS6du1qU6dOtXfffde1NxKRtq243nnnHddutb958+a2cuVKO+qoo+yJJ56w++67L9/7qP6yvkQorPbr189t05cG9WJ7tJ9/+ukn22+//dxlPX6bNm3cvtCXDE/37t3thRdecOG+JBFo80ENWgBAMtCQg+XLl9sJJ5zgLqvnTcFRvXX169d3vYmqv6vDzE2bNnW32bJliztsrMP6CkLz58+3ypUr26effuoK4nu9rAqEsn79encYWkHonnvuCT6391zqiWzYsKF7fAVaPe5dd92VKyCrLTqJyjmpd1RtDw20OhyusHb33XcHJxYpoDVp0sQeeuihXLWDdf6iiy4q8H3RIXPVQdV9Qyngq3dRr009tQq3kQbaSNumLxE//vhjoY/dtm3b4PuV19ChQ12vrMKsqBSdhlZoe0GBVj2uCqqh11evXt2OP/744GV90QilnmZ9MVHIDQ20p5xyit1///3uc+GF35JAoN0HatACQOpRz6I6N+JBC/nEstqCwqh6D3UoXK+zY8eO9thjj7neT1HY1PMrWKmMmXoUdQhe2xWK5LzzznMnj3p71Vun2ym8ehReFc7Uw+vR7V588UXX06rnzq93WEMBFH7VhkaNGrltGg+qIQBXXnmlu6weX03qUq9iURfC0GQ5L/CFUpitUaOGC2W6XsFMPdHq0S6K4rRNvaXel4WCqDe8oEA7a9YsN+QglHrQNWRAq+HlV5pOQyM6dOhgmzZtcl9OtE/0ZaZSpUoFtkGTv9SzruEieZ/LGyN96qmnWkkh0O4DNWgBIPUozDbvNyYuzz1nYCfLSI/dv+d27dq5MCsKrjpkPHbsWHdZk7MUWm+66SY3YUuBVycFKA0BCKVwqSCryVCq46vJVQoxoYFW43dDw6yo51Xh+KqrrnLDIdRze+6559q9994bnKDVqVMnO+igg+yNN95wh+tFgfvMM88MrjClHkAFNPUSFtW6dev2Gjsr6pFVmNb7oufp0qWL2/bss88W6fGL0zaFfJ0itWnTpr16RtXbqv2ofbb//vvvdR/tQ/Xka8y1hogsXbo0OMlNn5e8NFzisssuc720ob2zon2o3ne9xyWJQAsAQArJG3bKlSsXnDCkEKOQqcPIeWexhwabESNGuF5A9Z4qtOkxNCTAW9bUk9/yphpS8NZbb7mgqOfRZCRVYtBPjbVVj6ZOV199tQu0AwcOdG1R1YHQnkuv9zB0gli4dN+1a9fm2qZxwRpmoOEACnKiIK9hDurB1msUhV2Fw7y8bbq+OG0r7pCDcuXK7VVpwbtcvnz5fO+j7Xrv1bt78MEHu23avxpmoHAbSvtZIVbBWUNVvLHBHn2W9AVHw1RKEoEWAIB8DvurpzRezx0vCiEKZNddd50bR1uQW265xc3Y10+Pev7yBr3Chk7o0Ldm0uukXl2N81ywYIEdcsgh7vprrrnG9c5++eWXLmhqKIB6aD2aUKXeRF1XVHoOVVMIpYCtmft5H0+B7cMPPwxOctI4YoXOvBTm9Xp1fXHaVtwhB40aNdorhGpowAEHHFDgEALdR18+vDArCq3qTfeWqRX1Omu7Xr9KkHljqvO2X/ReliQCLQAAeSiYxPKwf6JSoFW5p5dfftmNhw0NpKoMoGEA6n3T4eTQwKJwE25vpAJPaHASr4c4dDytSkhpXOerr77qhjJoiELoRDLp1auX60nUeNX27dvnuk7t0TKrFSpU2KsNelxVUtiwYYMbeqCg9vbbb7txpgrWoTTRTWHXC7QK4K+88oo7RB9a2UG1WzXu1qtcEGnbijvk4Mwzz3Tt07AAPb72l4aLaEyyZ9GiRS7Qq5qDArsm4T388MO5xtiqAoTur+EKoWFWY2f1mjTZLD8ag6te+7wT7mIt9X5bAQBIIjqc7B0i9ygYaqZ7JDRhS4FPE6MUahU2NRxAvagKfAqVGluqeqV33HGHO3SvWfIKaOFQMFRvriovqIdPk9T0nOoV9noCPRrPqoCpYJ13opPXi6vSUgpxOjyuCWeqqqDJaF7Pbn6h8ZhjjnElzTQJrGfPnm7RA40vDe0B9mh8r4YceEFcY0f/9a9/2cknn2w33nije6+/++47NwxD446L27biuv3224OVDvTejR492n0Z0aQ6j4Zv6P3WflSvrc7rNamtuo96eJ988kl78MEHg18i9IVC7Vbw1esN7d3V++nRe6r9VtIItPnMat2WE5+ZrQAAFIVm32tsa95D1Kodq0CjST6hvaGqOZt3FSf1KmoSVuhjapUpBRz14il83nrrrbluo8CkslyTJk1yPXjqndQ2b7hAQc8lGqupYKReXYVZhViFwfx6JRUmFSDVE+pVO8jr0UcfdeW9NCxAYzpVL1WTlVR3tbA6r//85z9dqTEt7KDyZaGT0kIpiKqygsbY6r3U+F5Nonv//ffdWFeFVvVW6/q8k8AibVtxVK1a1e037R+916o6oLqwob3Jei81Vrhs2bLBcbeqdKCeXS2moH2u0mahi3JoP6mHNu9QDX3x8QKt3gONi877BaskpAXyG9mc5PQtTB8qDWj2Prx6G7pmZdu0JRtKdLYpACD+1Avp9cAVNHEGyUc1bRU4i1qWC/lTJQr1+BZWZ7ew37X88lm4SGr/Tz2zecNs6/rV4jo4HwAAxI6GEiB68hsWUlIItPmY2rejZaSXjnlxawAAABQfgTYfCrMMMwAAAPCHoq3HFiMqAaFByEVZVSKS+wAAACD5xDXQajUSlYjQjD8VZ9asTK0WEu37AAAAIHnFdchB//79XWkQ9baqHMTnn3/uCv+qqLNO0boPAADhSMHCP0BS/I7FtYf2zTffdMV3vULK6nk98sgjXe27aN4HAIDCeMXjtRoSgNjRkXbxauD6vodWq1ZodZFWrVrl2q7LKsobrfvIjh073Cm0zhkAAB4t/6mVrvQ/Rv9oVTwfQHR7ZhVmf//9d7f4Q95ljH0baLV+suy33365tmvFEe+6aNxHBg8ebAMGDIhCqwEAyUglGmvVquUKvi9ZsiTezQGSVtWqVd2Sx9EWt0Cbnp7ufm7fvj3XdqV377po3Ed69+5tvXr1ytVDq8lkoVRzVquCeecBAKlF/0eaNGnCsAMgRnT0I9o9s3EPtAqUOqSzfPnyXNt1uUGDBlG7j7dGsU77+nZO7VkASG36H8PSt4D/xG2QUIUKFezEE0+0jz/+OLhty5YtNm7cODvttNOC2+bOnWuTJ08u0n0AAACQOtICcaxRMmnSJGvfvr3ddNNN1rZtW3vhhRdszZo1Nm3aNKtYsaK7jSoaKNDOnj077Pvsi4YcZGZm2qZNm6xKlSoxfY0AAACIbT6Lax1a1Y2dOHGivfjii64clwLqXXfdlSuYNm/e3A0HKMp99sXL8FQ7AAAASAxeLoukrzWuPbTxojG3eSeFAQAAIP6WLVtmderUKdJ9UjLQ7tmzx1auXGmVK1fO1fvrVT/QG8lQhOTD/k1+7OPkxz5Ofuzj1N3HgUDAzY2qXbt2kWtBx3XIQbzoTSos+evNJdAmL/Zv8mMfJz/2cfJjH6fmPs7MzIzosVgKBQAAAL5GoAUAAICvEWhDaPGF/v3773MRBvgT+zf5sY+TH/s4+bGPk1+5GOStlJwUBgAAgORBDy0AAAB8jUALAAAAXyPQAgAAwNdSLtDOnTvXZsyYYTt37ozpfRAf2kfaV9pn4dq4caO7z7p162LaNkSvIPfUqVNt6dKlRb7vlClT7Mcff2RXJLjVq1e7/fTHH3+EfR9NB/nPf/5j8+bNi2jZTJSsRYsW2bRp0+zPP/8M6/a7d++2X3/91aZPn27r16+PeftQfLNnz7bJkycXadEr3efnn392+7vIAinit99+Cxx++OGB/fffP9CgQYNAjRo1AuPHj4/6fRA/X3/9tdtH2lfVq1d3+077sCBz5swJdO7cOVCtWrXAkUceGahYsWLg3HPPDWzatKlE243wvfDCC4EKFSoEmjZtGsjIyAicc845gW3btoV139deey1QqlSpwEEHHcRbnqB2794duP766wPlypULNG/e3P3s06fPPu+nv8sNGzYM1KlTx/0uH3vssYHFixeXSJtRNBs3bgy0b98+ULly5cAhhxzifr7zzjuF3mfChAnu77r271FHHeX+BlxzzTWBXbt28fYnoDfffDPQqlUr9781MzMzrPv88ssvgcaNGwdq1qzp/kbXq1cv8OOPPxbpeVMm0J5wwgmBDh06BHJyctzlO++804WewsJLJPdBfGifaN/84x//cJe1z/RH88QTTyzwPiNHjgyMGjUqeHn16tXun+J1111XIm1G0eiPW1paWuDDDz90l1etWuX+wd199937vO/cuXPdH0mFJQJt4nr++efdP0DtL5k0aVKgbNmygREjRhR4H91WAWfgwIHBbTNmzAhMnDixRNqMornqqqvclxUFW3nppZfcPl6wYEGB99Hf5csvv9x94ZFZs2YFypQpE3j77bd5+xNQnz59AlOnTg0899xzYQVa7dcWLVoEunbtGtizZ4/bduWVVwbq168f2LFjR9jPmxKBdv78+Tr+FPjqq6+C29atW1foL0Qk90H8vPXWW+6P4oYNG4LbRo8e7fbhr7/+Gvbj3HXXXYFmzZrFqJUojptuuilw2GGH5dp2//33uyMo3h/B/Gzfvt311g8bNizwwAMPEGgTmHp1evTokWvb6aef7o6kFERBR/8MC/sMIDHoaEr58uUDWVlZucKMjqz179+/wPupF1dfdkLp9/7JJ5+MaXtRPOEGWn1x1f/qn376KbhNX3C07Ysvvgj7+VJiDK3GR8rRRx8d3Fa9enVr2LBh8Lpo3Afxo32ifVO1atXgtmOPPTZ4Xbg0NrNx48YxaSOKR/sx9PfR28ca+7x8+fIC73fnnXfaEUccYRdddBG7IIFpzNysWbPy3ceF/Q6PGzfOunTpYjt27HBjMpctW8YY2gSluQ1//fVXrn1cqlQpd7mwffzQQw/ZE088YUOHDrWxY8fa9ddfb7Vq1bIrrriihFqOWNK+L1OmjB1++OHBbY0aNbL99tuvSP+/y1gK0ADy0qVLW2ZmZq7tCqgFDS6P5D6IH+0T7ZtQCrf6Yxnu/srKyrLvv//eJkyYEKNWItr72Lus6+rWrbvXfT766CP74osv7KeffuLNT3Bbtmxxkzrz28cF/Q7rKKMmkK1YscIOPfRQ9w9wyZIldsghh9iwYcOsQYMGJdR6hMPbj/nt48WLFxd4P31h+fjjj+2ee+6xAw44wH1pUcjN+zjw7+dCv7tpaWnFylsp0UNbtmxZ9+0/b5WC7du3W3p6etTug/jR/tI3/1A5OTlu1mQ4+2vEiBF266232ssvv2xt27aNYUsRzX2s30fJbx9v3brVrr32WuvZs6fNnDnTfVlRZQR9LnSeqhaJt38lv31c0O+w/gGq4+Gzzz6z8ePHu94chR1tu+6660qk3YjtPlbPe/v27V2PnX5/9eX0u+++s7vvvtuGDBnC25+kf9sjyVspEWjr16/vfq5cuTLXdl2uV69e1O6D+NH+Ui9NKO/yvvaXevEuvfRSe/755+2aa66JaTsR/X2sUJNf76yCa/Pmze2TTz6xe++9153GjBljmzZtcudVGgaJo2LFiq5HJr99XNjvsHphTz31VDfkyHuc7t27uy8tlO9KLN7/1aLsY5Vx+u2339wXU31REf1eK+TqdxvJ8bnYvHmzO0oT+vd77dq1RcpbKRFo1eOmP3KhH/7s7Gz7/fff3R9Cj+rbLVy4sEj3QWLQPlmzZo398MMPwW0jR460SpUqBXtc1eOuf3Lah6G3ufjii+3ZZ59147KQ2Pv466+/zlW3UvuvTZs2bj97NYW1j/XNXoewdD70pF47HbLU+Q4dOsTx1aCgffzpp58GL+t3Vr2voX9z1UsXWtuyU6dOewUkjanef//99zqEifjSlw/NUQj9v7pq1Sr3dzt0H8+ZM8cFWdHvq+QdJ6/L3nXwH9UE92qJ68uJxtCG/u6r80GhtmPHjuE/aCBFPPLII67OqEqEaLZzo0aNAuedd16u2xx66KGBG264oUj3QeLQvlEdO+2rF1980e27Rx99NHi9KiDoI//GG2+4y2PHjg2kp6e7MjLfffdd8JSdnR3HV4GCbN26NdCkSRNXSu/jjz8O9O7d21UdUf1hj2bEah97ZZ/yospBYlMtSv3eqrzaJ598EujWrVvggAMOCCxfvjx4G82GD505ret0m1tuuSUwZswYN/NdNYo1wxqJZ/jw4e739sEHH3Ql+FQzWNUtdu7cGbyNqlro99yjetN169Z1f7v1O37ttde6x/jhhx/i9CpQGNV41//SO+64I1CpUqXg/1b9DfeozOZ9990XvKzyi9r2+uuvu0pStWrVcn8HiiIlJoXJP/7xDzvooIPsvffec2Ny1FNz++2357qNZlqGznAP5z5IHO+++649/fTT9vrrr1u5cuXceNjLLrsseL2+AR5//PFWo0YNd3nBggV2zDHHuNVndAjaU7lyZTeRCIlFR0zUs/rwww+7HvUDDzzQ9dieeOKJwdtUq1bN7eOMjIx8H0OHr7zqF0g8OpSsI2FPPvmk+11u0qSJ643V3+HQfRg6zl3XqYfvscces0cffdTNfv/ggw/szDPPjNOrQGEuuOACGzVqlBv/qt/fU045xU320t9nT4sWLVzvnOf999+3V155xfXW69C0xtPqiGrLli15sxPQ22+/HZxcrQoz3v/Xf/3rX8GMpSNr3hAU0d91XTd8+HA396V379524403Ful505Rqo/pKAAAAgBKUEmNoAQAAkLwItAAAAPA1Ai0AAAB8jUALAAAAXyPQAgAAwNcItAAAAPA1Ai0AAAB8jUALwJkxY4Z99913vnw3du7cacOGDbMNGzYU6zbJ7Oeff3b7uCRoCctFixYV+zYl0Y68Pvroo+CSnCX93OFQ4fnQ5Z8B/BcLKwBJ4o8//rCxY8fmu366VmXZF62Cp9XTtIpPLGhtdm999tKlS7sVno488sgCV/Uqio0bN7pVwn788Udr3bq1W2Xoww8/tNNPP92qVq2a721iRaE5dNW5Qw45xK14VVQKQ7/88oudddZZxW6TAlCzZs1sxIgRbnW8FStWBL+8pKWludXzDj/8cNtvv/0sGvSZ69u3r/Xo0cNd/uSTT9yqTgcffHCBt4mFSJ5j//33t+eff94uvvjiEn/u0HXuFy9ebGeccYZlZmbmuu7qq692q6E99NBDxWofkGxSZulbINlpCd9LLrnELfmpIOXR0rDhBNpYU8+SlidVQNu9e7fNnDnTLWOpAHjyyScX67HT09PtoosuCgYyPa7eC/VIKjTnd5tY0fNqeV2FN7Xj22+/dftEr1NBPlxaFnTQoEFRCbTPPfecC7QKs6JQr3aef/75VrZsWVu4cKHNnTvXLSl8zTXXFPv51GYtT+q56aab3GsJDbR5bwOzjz/+2O6//373BURfLmfNmrVXoNUyovpM33HHHXbAAQfwtgH/j0ALJJknnnjCmjZtmmubwooOOYv+QR522GFWt27dfT6WDr3qn6pCYKtWraxcuXK5rlePq/7x1qlTx4466qh9Bjb9A/Z6MBVqzznnHNeDpTAuq1evth9++ME9z9/+9rdcwVx27dplkydPdr2tWiPcew0KZeeee67rgfUOG8uYMWPsP//5j+t1U2gOvY16DfU+qQc11MiRI13487Zv2bLFsrOz3friChI1a9bc5/umNcivuuoqd16h+uijj7Z///vfdvnllwdf5zfffOPOV6hQwQ499NBc+0w9qAqdCjbe+6XXq3bJkiVL7KeffrLq1au7/VJYL7dWN3/xxRftkUce2eu61157LdiDrbXTFTy1T/S4et2TJk2yHTt22HHHHed6cfOaPn26LV++3PVAe22T0047LbhOu/bB9u3bXa9j+fLl3WekW7duuW6jdd91nb4IhNKXAW3X84t63rX/N23aZM2bNy9yIP7qq69s3bp1rldavZz6zOb9jHlWrlzpfmeqVKniPou6T6jitiU/eq+13r2obfnRZ0W96dp3CrcA/osxtEAKUGBU749OTz/9tAtP+zpk+fDDD7vg+8ILL7h/nAobXvBU0OrSpYs7pP/666+7oKbrFQLCpWCjXjoF4q1bt9pLL71kDRs2tKeeesr69Onjws64ceOCt9djKzT17NnTXn31VWvfvr3169cv2B71OKqnUb744gv3c/z48e41Kxjlvc27777rnifv+6TQq/aI7qtDx+pdfOaZZ9z7pl7MolAwUQj2vlDImjVrgvtjyJAhLjBddtllLnzKqlWrXBDetm1b8HZqm66/7bbbXIh95ZVX7M4773RtUvgtiJ532bJl7v0qjF63ApW+AOh9r1evnt1333325JNPup7VrKys4G11u1NOOcXdR/v/wgsvdL29XvtvueUW18Ps7QMFWoVfvQ6NLc17G31huPTSS/cKjOedd54bdiEK8PqSoaExeu066qDPQlHoc6A2aDiK3rvGjRvb999/v9ft9Jr0eVbPtl7jqaee6l6zJ5K26D76ElUYHUHwjigURu99rIYGAb4VAJAUsrOzlSYCTzzxRODdd98NntavX7/XbX/66adAuXLlAvPmzQtuu+222wKdO3d253ft2hUoX7584PPPPw9e/+uvvwZmzpzpzt90002B0047LbBjxw53effu3YELL7wwcNFFFxXYvv79+wfq16+fa9udd94ZqFy5cmDRokWB9PT0wNChQ4PX3X777YF69eoFtm/f7i4PGjQocNxxxwX27NnjLuvnxx9/7M5v2LDBvfYff/zRXV67dq27PGPGjODj5b3Np59+6t6DjRs35mpjs2bN3PklS5YEKlasGBg3blzw+unTp7v3Zfbs2QW+Tj3HG2+8ket59doeeeSRAu+j9taqVSswfPjw4LZXX311r/frlVdeCTRq1Ciwbt264Da9L02bNi3wsV977bXAfvvtl2vbRx995Nqptnnefvttt23OnDmBunXrBu66667gdf/617/ce6X9JKNGjQpUq1YtsHXr1uBttC/0ORC1W+33HHTQQbnek7y3Wbp0aSAtLS0wefLkXG3Ue71p06ZATk5OoEGDBoGnnnoqeP3vv//u3jN9xguStx15Pfjgg4HDDjss17bq1au7+2mfyKpVqwI1a9YMPPnkk+5yuG3J+9z6rOt9CIc+t9oXs2bNyvf6d955x+0PAP/DkAMgyah3LfQwqnqadJhdh5DV66feQR3u16HmadOm7XXIXUqVKuUO+2tIQadOndxl9WaJ7vv222+7oQLqcVKG00mTvN55551C2+YdQtfhe/VYqQdMvazqMVMvpnopPZpQo95kHfZWj5QOzWvimw5xa6iBDgHr8Hik1Lus90kTpbxxoxoW4A0VUDv1Hq1fv94++OCDYO+jhi/oEHmLFi0KfGzv8LrG0L7xxhuutznv2FRVXdD7r55n9UbqNWm4xQUXXFDg4+qxdLhZvZ7e+16pUiXXq6peXR1Gz0uH2L1hFnnpfddwBU1A0lAVPbfaox5dDUHwXHHFFfbPf/7TDeXo1auX2xfqsZw3b57rLZbi7Au99pNOOsl9frzhBTqvHnwd8v/yyy/dMIsDDzzQjcX2XrsO8+u9KMoELg2j0RAcDVvRUBV9xtUTHjpsQxOvtJ9Fn0u9/vfff9+NW9XzRdIW9dSrpzoatD/1/ut3uqAhE0CqIdACKTCGVodZ9U9ah44VrhRW//rrL/v999/zfQyFxaFDh7pDqo8++qgLGzocqkPLCpX6R6pJXXmHGCh4FsY7hK7hBgpfn332mXXs2NFuvfVWN9wglMZxaryvwoPccMMN7rC1xhBq6IEOA998881u/G4kypQp416PgpPCpsKkhj94ofq3335zY3YVWkIdf/zxLswURu1UeTB9edB5jXf0ApJoXLIminnjZytWrOhuW9D+8KhNCr9526R9o+35UeAtqMyT3n9NltPYZn250ONoDKeCfOjkOX0etH+8faH9rH2mnxpb26FDB7v++uvDOlxeEL3v+hKjIQ76nOiQ+nvvvRd83Wpn3kP2+hKV3xeygmiYg74UaHKc9ocXMNeuXRsczysaZhJKvzf6Electuj1hX5hKw7tT33JjEaFECBZEGiBFKDwoZ5Q9TB51PPk9TrmR2NkdVLI05hUjRHUP3OFSP0zVQjMO+5xX0InhYVSuFBPaN4eTAVnLwgq9ClkK+xMnDjRje3VZCu1L1Ldu3e3E044wU3CUrDVeS/YqGdQp/zaW5RJYbq/xhgr7HiTnjQ2VWHwzTffzNVjXNj+8Nqk++lLRrj0vApsCkF6DwuaFObR+633XWFeod+j/RMaygcPHmwDBw50vczq2dZr0xGAwnquC6OJYgqcmril3ma1VWWrvNetwK7xxgrokdAXC31mNBbZm8ClSV0K9Xnf97y1inXZe+3RaEtxqUddR0yKUjUDSHZMCgOSnP5Zq+dPPYEeTYRRj2BB1HvrBUz941TQ0AQdBQD1CqkU2Msvv7xXEFAwjISCpA79zp8/P9fhcB0S9mrGeo+t51fvrHoU9br0zz0vL2jodRSmbdu2rvdNQVm9gQq4oQFT4Sd0YppowpiGEoRLh6D1WJrM5VGVg9D9ocsK6XlfQ97263EUvBU4w33fNeFMPYrad+FQ76WCrKo9eDSkQZOztJ+89mroifaPJkRpopwOfSvc5ie/15KXgrV6rfX6dFLvuR5f2rVr516DJmCFUhsK+xyHUpvVIx7aE5u3p9ujowgefcY11EI988VpSziTwsKlz4qObAD4H3pogSSnw8Vnn322653V2EgNGdCM/by9daHUm6exjBrDqIL4GlqgMYQKsaKeLs2a1z93BQ/1WGnGug7/q1pBUXkz5lXKSWM0FRhVZko9md64UFU20Cz1zp07u8Ph6hVUFQYNP8h7SF3jV9VTqJ5MjQvVYfGCFlNQL7OqGOg1qJfQo9fmlbFSoFewV+BW0P78889dT124VDFCY181Xlft0WvV0BC1U+FR4Ty0N1Q0NlU9q2qbnltlu9TLroCt0Hnddde5cK/xulqEQeN686Pb6DWqqoOGBuyL3m9Vf9AQFQVZBVW1VW32KiUoUKl3tmvXrq4agsaO6nNW0JATvfeqHKC2KFSGvs+h9IVCvdkKv3r80KMJGk+t/aA26bOpsdR6P1WtQ0cS9kVfXhSs9XnV7fW+aWx0fhTe9dr0edTQBx0F8MJvpG3Rlyb11ut3sSAak6xebm9ox+jRo90XPR2J8BbnUJkwlULT2HIA/0OgBZKEDolqDGR+QUuHthU0VR5JYVCBTP9cQ3sJFaBq164dHL+q3jaNp1R40SQUDTvweugUFufMmePGI6pklJ5b4c87RJwfhc/CFglQD+lbb73leo/VA6bAGnp7FZxXoFXZJ/3j13U6rK9evPwWTVBvmAK42q1eWPVU5rewwpVXXumCqtqXd/KUgrsCrQ5L631QvVH9LGwMrZ4jdAEB7/1S2NG4YwValUFT+Nfr0XhmfcFQT7OGcoQOFdB+0utQwFIYVHjXWF+FI2/imXrq9jUpyiu7pjao7XputVPvW34UnDWJSa9bQb9///5uYpRHr0Ft0WdIr0GBWz2Q3njmvIsmqAdXtXAVxtXjqUCb38IK+rKiIwEKvdpfoTTkReFRnxOFd43pVSAN/QznFfoc2rfqpdbvgWoA6356HH1hCP1yp/JjCtWaOOZNmlQpOQX3orQl7+sLZ1KYV15PtH+mTp3qTvrMeoFWX+z0xaGgOrVAqmLpWwBIAQqU+sKinlb4l8awa/hKJMspA8mMQAsAAABfY1IYAAAAfI1ACwAAAF8j0AIAAMDXCLQAAADwNQItAAAAfI1ACwAAAF8j0AIAAMDXCLQAAADwNQItAAAAfI1ACwAAAF8j0AIAAMD87P8A3/yw2qUt0pYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display = metrics.RocCurveDisplay.from_estimator(model_svc, test_X_logistic, test_Y_logistic)\n",
    "display.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24ad3efa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:46:18.912408Z",
     "start_time": "2022-08-23T13:46:18.419382Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Coefficient Plot'}, xlabel='Fitted value', ylabel='Residual'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x1700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coefplot(vars_logistic, model_svc.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08163eba",
   "metadata": {},
   "source": [
    "#### Optimizing C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0be5d6",
   "metadata": {},
   "source": [
    "The above classifier just took the default of `C=1`.  However, like with LASSO and related models, `C` is a hyperparameter.  Unlike LASSO and related models, `sklearn` does not provide us with a built-in CV function.  Instead, we will need to use `sklearn`'s built-in cross validation constructors to customize a cross validation for this.  This is built into the same `sklearn.model_selection` library that we used for splitting data in Session 1.\n",
    "\n",
    "Note that the `model_selection.GridSearchCV()` function allows for parallel processing -- you can specify the number of processes to use using the `n_jobs=` parameter.\n",
    "\n",
    "Also note that `model_selection.StratifiedShuffleSplit()` is a bit more complex than the standard cross validation we did in session 1, in that it is making *stratified* random samples across classes.  This is rather important in our case, since there is pretty severe class imbalance (less than 3% of data is an irregularity).  If you want to directly replicate the method from before, `model_selection.KFold()` will do the trick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3da9b445",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:46:25.890860Z",
     "start_time": "2022-08-23T13:46:21.305762Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best parameters are {'C': np.float64(0.01)} with a score of 0.99\n"
     ]
    }
   ],
   "source": [
    "C_range = np.logspace(-2, 6, 9)\n",
    "param_grid = dict(C=C_range)\n",
    "cv = model_selection.StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=1)\n",
    "grid_svc = model_selection.GridSearchCV(svm.LinearSVC(dual=False), param_grid=param_grid, cv=cv, n_jobs=20)\n",
    "grid_svc.fit(train_X_logistic, train_Y_logistic)\n",
    "print(\"The best parameters are %s with a score of %0.2f\"\n",
    "      % (grid_svc.best_params_, grid_svc.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5910fb36",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:46:26.041376Z",
     "start_time": "2022-08-23T13:46:25.951861Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x188939c9bd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display = metrics.RocCurveDisplay.from_estimator(grid_svc, test_X_logistic, test_Y_logistic)\n",
    "display.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "95d204ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:46:26.581367Z",
     "start_time": "2022-08-23T13:46:26.102390Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Coefficient Plot'}, xlabel='Fitted value', ylabel='Residual'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x1700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coefplot(vars_logistic, grid_svc.best_estimator_.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15891194",
   "metadata": {},
   "source": [
    "#### Visualizing SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c83097",
   "metadata": {},
   "source": [
    "Given the high dimensionality of the SVC algorithm, it is difficult to visualize.  To aid in this, the below visualizations implement a dimensionality reduction technique (UMAP) and show the resulting probabilities embedded into the 2-dimensional projection from UMAP.  This will allow us to see if there are any clear patterns in our data as it relates to our dependent variable.\n",
    "\n",
    "Note that `LinearSVC` does not provide probabilities.  However, it reports a score that roughly approximates the output of a logistic regression via `.decision_function()`.  Thus, applying a logistic (i.e., sigmoid) function of $f(x)=\\frac{1}{1+e^{-x}}$ to the output will convert to probabilities.  Note that there is some weighting internally applied within LinearSVC which we are unable to recover, introducing some potential error in the below charts.\n",
    "\n",
    "Probabilities are fully recoverable under `SVC()`, however, `SVC()` uses a \"hinge\" loss function whereas `LinearSVC()` uses a \"squared hinge\" loss function.  As the problem we are focused on is quite sensative to the choice of loss function (i.e., performs poorly under hinge loss), we will use the output from our `linearSVC()` above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "25a4b56e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:46:29.461528Z",
     "start_time": "2022-08-23T13:46:29.451445Z"
    }
   },
   "outputs": [],
   "source": [
    "train_Yhat_logistic = logistic(grid_svc.decision_function(train_X_logistic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f4b4f419",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:46:49.059641Z",
     "start_time": "2022-08-23T13:46:30.146062Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: title={'center': 'Predicted values'}>,\n",
       " <Axes: title={'center': 'Actual values'}>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "umap_compare_svm(train_X_logistic, train_Yhat_logistic, train_Y_logistic,\n",
    "                 clip=[[0.25, 0.3], [0, 1]], binary=5,\n",
    "                title=\"Full sample\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b5b298ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:46:52.736622Z",
     "start_time": "2022-08-23T13:46:49.120930Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: title={'center': 'Predicted values'}>,\n",
       " <Axes: title={'center': 'Actual values'}>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "umap_compare_svm(train_X_logistic, train_Yhat_logistic, train_Y_logistic, clip=[[0.25, 0.3], [0, 1]], cmap='coolwarm', binary=5,\n",
    "                 subset=((train_Y_logistic==1) | (np.random.rand(len(train_Y_logistic))<0.05)),\n",
    "                title=\"Performance on actual irregularities (Large) and random sample of non-irregularities\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d57837bb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:46:56.217208Z",
     "start_time": "2022-08-23T13:46:52.797624Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: title={'center': 'Predicted values'}>,\n",
       " <Axes: title={'center': 'Actual values'}>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "umap_compare_svm(train_X_logistic, train_Yhat_logistic, train_Y_logistic, clip=[[0.25, 0.3], [0, 1]], cmap='coolwarm', binary=5,\n",
    "                 subset=((train_Y_logistic==0) & (np.random.rand(len(train_Y_logistic))<0.05)),\n",
    "                title=\"Performance on a random sample of non-irregularities\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f86a99f5",
   "metadata": {},
   "source": [
    "### SVR: Support Vector Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16fd591f",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Notes:\n",
    "\n",
    "- I am specifying `dual=False` here because we have more observations than regressors.  If you have more regressors than datapoints, set `dual=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d29af15e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:47:32.159183Z",
     "start_time": "2022-08-23T13:47:32.120164Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
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       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVR(C=1, dual=False, loss=&#x27;squared_epsilon_insensitive&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVR</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html\">?<span>Documentation for LinearSVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('loss',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-loss;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=loss,-%7B%27epsilon_insensitive%27%2C%20%27squared_epsilon_insensitive%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27epsilon_insensitive%27\">\n",
       "            loss\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-loss;\">\n",
       "            loss: {&#x27;epsilon_insensitive&#x27;, &#x27;squared_epsilon_insensitive&#x27;},             default=&#x27;epsilon_insensitive&#x27;<br><br>Specifies the loss function. The epsilon-insensitive loss<br>(standard SVR) is the L1 loss, while the squared epsilon-insensitive<br>loss (&#x27;squared_epsilon_insensitive&#x27;) is the L2 loss.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;squared_epsilon_insensitive&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual;\">\n",
       "            dual: &quot;auto&quot; or bool, default=&quot;auto&quot;<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples &gt; n_features.<br>`dual=&quot;auto&quot;` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features` and `loss`. If<br>`n_samples` &lt; `n_features` and optimizer supports chosen `loss`,<br>then dual will be set to True, otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br>   The `&quot;auto&quot;` option is added in version 1.3 and will be the default<br>   in version 1.5.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('epsilon',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-epsilon;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=epsilon,-float%2C%20default%3D0.0\">\n",
       "            epsilon\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-epsilon;\">\n",
       "            epsilon: float, default=0.0<br><br>Epsilon parameter in the epsilon-insensitive loss function. Note<br>that the value of this parameter depends on the scale of the target<br>variable y. If unsure, set ``epsilon=0``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
       "            intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes `[x_1, ...,<br>x_n, intercept_scaling]`, i.e. a &quot;synthetic&quot; feature with a constant<br>value equal to `intercept_scaling` is appended to the instance vector.<br>The intercept becomes intercept_scaling * synthetic feature weight.<br>Note that liblinear internally penalizes the intercept, treating it<br>like any other term in the feature vector. To reduce the impact of the<br>regularization on the intercept, the `intercept_scaling` parameter can<br>be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1000</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=coef_,-ndarray%20of%20shape%20%28n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            coef_: ndarray of shape (n_features) if n_classes == 2             else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>`coef_` is a readonly property derived from `raw_coef_` that<br>follows the internal memory layout of liblinear.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](31,)</td>\n",
       "           <td>[ 0.05,-0.01,-0.01,...,-0.01,-0.02,-0.05]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%29\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            intercept_: ndarray of shape (1) if n_classes == 2 else (n_classes)<br><br>Constants in decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
       "           <td>[0.]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>31</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVR.html#:~:text=n_iter_,-int\">\n",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>4</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "\n",
       "    const parent = element.parentElement;\n",
       "    if (!parent || !parent.nextElementSibling) {\n",
       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "/**\n",
       " * Copy the list of feature names formatted as a Python list.\n",
       " *\n",
       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
       " *   contain a `details` element and a table containing feature named.\n",
       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
       " */\n",
       "function copyFeatureNamesToClipboard(element) {\n",
       "    var detailsElem = element.closest('.features').querySelector('details');\n",
       "    var wasOpen = detailsElem.open;\n",
       "    detailsElem.open = true;\n",
       "    var content = element.closest('.features').querySelector('tbody')\n",
       "                  .innerText.trim();\n",
       "    if (!wasOpen) detailsElem.open = false;\n",
       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
       "    const originalStyle = element.style;\n",
       "    const copyMark = document.createElement('span');\n",
       "    copyMark.innerHTML = '✔';\n",
       "    copyMark.style.color = 'blue';\n",
       "    copyMark.style.fontSize = '1em';\n",
       "\n",
       "    navigator.clipboard.writeText(formattedText)\n",
       "        .then(() => {\n",
       "            element.style.display = 'none';\n",
       "            element.parentElement.appendChild(copyMark);\n",
       "\n",
       "            setTimeout(() => {\n",
       "                copyMark.remove();\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'orange';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-2');</script></body>"
      ],
      "text/plain": [
       "LinearSVR(C=1, dual=False, loss='squared_epsilon_insensitive')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svr = svm.LinearSVR(C=1, dual=False, loss='squared_epsilon_insensitive')\n",
    "model_svr.fit(train_X_linear, np.ravel(train_Y_linear))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "720009f7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:47:33.090209Z",
     "start_time": "2022-08-23T13:47:32.930695Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Coefficient Plot'}, xlabel='Fitted value', ylabel='Residual'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x775 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coefplot(vars_linear, model_svr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "46653a23",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:47:37.125724Z",
     "start_time": "2022-08-23T13:47:34.033211Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best parameters are {'C': np.float64(0.0001)} with a score of 0.06\n"
     ]
    }
   ],
   "source": [
    "C_range = np.logspace(-4, 6, 11)\n",
    "param_grid = dict(C=C_range)\n",
    "cv = model_selection.KFold(n_splits=5)\n",
    "grid_svr = model_selection.GridSearchCV(svm.LinearSVR(dual=False, loss=\"squared_epsilon_insensitive\"), param_grid=param_grid, cv=cv, n_jobs=8)\n",
    "grid_svr.fit(train_X_linear, np.ravel(train_Y_linear))\n",
    "print(\"The best parameters are %s with a score of %0.2f\"\n",
    "      % (grid_svr.best_params_, grid_svr.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e8246759",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:47:37.366245Z",
     "start_time": "2022-08-23T13:47:37.217725Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Coefficient Plot'}, xlabel='Fitted value', ylabel='Residual'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x775 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coefplot(vars_linear, grid_svr.best_estimator_.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "97d8ea8e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:47:37.441245Z",
     "start_time": "2022-08-23T13:47:37.427241Z"
    }
   },
   "outputs": [],
   "source": [
    "train_Yhat_linear = model_svr.predict(train_X_linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3cc1ab42",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:47:41.716888Z",
     "start_time": "2022-08-23T13:47:37.502242Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: title={'center': 'Predicted values'}>,\n",
       " <Axes: title={'center': 'Actual values'}>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "umap_compare_svm(train_X_linear, train_Yhat_linear, train_Y_linear, clip=[[0, 2], [0, 2]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29bcff26",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cf6b044",
   "metadata": {},
   "source": [
    "This algorithm differs from the previous algorithms we have looked at primarily by being non-linear (unless you request it to be linear).  Furthermore, it is tree-based and leverages a technique called \"boosting\" -- a type of ensembling.\n",
    "\n",
    "XGBoost is a flexible algorithm supporting manyt functional forms (both regression structures and loss functions).  It supports a variety of linear, binary, count, survival, multiclass, ranking, gamma, and tweedie forms."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af9cc90c",
   "metadata": {},
   "source": [
    "### Implementing XGBoost in python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ebe44d3",
   "metadata": {},
   "source": [
    "Python has a very good library for XGBoost, aptly called `xgboost`.  We will use this library to both fit and visualize our models.\n",
    "\n",
    "Note: The below does not exhaust all possible parameters.  There are also $L_2$ and $L_1$ regularization options as well as sampling parameters, among others.  See a [full list here](https://xgboost.readthedocs.io/en/latest/parameter.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97cc2e89",
   "metadata": {},
   "source": [
    "The algorithm requires that data is passed to it in its own format.  It can take our current datasets as input, and will create its desired format using the `xgb.DMatrix()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "24b10a1d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:48:54.581288Z",
     "start_time": "2022-08-23T13:48:54.553288Z"
    }
   },
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(train_X_logistic, label=train_Y_logistic, feature_names=vars_logistic)\n",
    "dtest = xgb.DMatrix(test_X_logistic, label=test_Y_logistic, feature_names=vars_logistic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3703280",
   "metadata": {},
   "source": [
    "There are many parameters to set for the model.  Every parameter has a default value, so you can focus on just those that are needed for your usage.  Below is a sample starting point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0d69504b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:48:55.991288Z",
     "start_time": "2022-08-23T13:48:55.987288Z"
    }
   },
   "outputs": [],
   "source": [
    "param = {\n",
    "    'booster': 'gbtree',             # default -- tree based\n",
    "    'nthread': 8,                    # number of threads to use for parallel processing\n",
    "    'objective': 'binary:logistic',  # binary, output probabilities\n",
    "    'eval_metric': 'auc',            # maximize ROC AUC\n",
    "    'eta': 0.3,                      # shrinkage; [0, 1], default 0.3\n",
    "    'max_depth': 6,                  # maximum depth of each tree; default 6\n",
    "    'gamma': 0.1,                    # set above 0 to prune trees, [0, inf], default 0\n",
    "    'min_child_weight': 1,           # higher leads to more pruning of tress, [0, inf], default 1\n",
    "    'subsample': 0.8,                # Randomly subsample rows if in (0, 1), default 1\n",
    "    'colsample_bytree': 0.8,         # Randomly subsample variables if in (0, 1), default 1\n",
    "    'random_state': 70\n",
    "}\n",
    "num_round = 30"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd2e9ab",
   "metadata": {},
   "source": [
    "Next we can train the model using `xgb.train()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "39a6e907",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:48:58.826287Z",
     "start_time": "2022-08-23T13:48:58.008288Z"
    }
   },
   "outputs": [],
   "source": [
    "model_xgb_logistic = xgb.train(param, dtrain, num_round)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b40274a6",
   "metadata": {},
   "source": [
    "We can check the ROC AUC just like we did with prior models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3b75dffa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:48:59.291344Z",
     "start_time": "2022-08-23T13:48:59.276345Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5940013491775207"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_Yhat_xgb_logistic = model_xgb_logistic.predict(dtest)\n",
    "auc = metrics.roc_auc_score(test_Y_logistic, test_Yhat_xgb_logistic)\n",
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "31e73d93",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:49:00.206345Z",
     "start_time": "2022-08-23T13:49:00.065345Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x188adc32490>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = metrics.roc_curve(test_Y_logistic, test_Yhat_xgb_logistic)\n",
    "display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=auc)\n",
    "display.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612b511b",
   "metadata": {},
   "source": [
    "To aid with interpretation, XGBoost also can output a chart showing the relative importance of each variable in the model.  This can help us to see which data is important for the model.  Note that since the model is non-linear, there are no signed coefficients.  The chart simply tells us which variables have the greatest impact on its classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e539a7f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:49:02.801447Z",
     "start_time": "2022-08-23T13:49:01.797447Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Feature importance'}, xlabel='Importance score', ylabel='Features'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,16))\n",
    "xgb.plot_importance(model_xgb_logistic, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee346b5f",
   "metadata": {},
   "source": [
    "### Seeing the trees"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf3c634a",
   "metadata": {},
   "source": [
    "There are two ways to see the tress in the model:\n",
    "\n",
    "1. Dump the model to a text file\n",
    "2. Use graphviz to draw the trees (which you can output to the notebook or save to a file.\n",
    "\n",
    "Both of these methods are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d30d8631",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:49:08.921981Z",
     "start_time": "2022-08-23T13:49:08.907979Z"
    }
   },
   "outputs": [],
   "source": [
    "# If you want to see the final tree model, this will dump it into a file in the same folder as this notebook.\n",
    "model_xgb_logistic.dump_model('dump.raw.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "83bd92cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:49:09.792493Z",
     "start_time": "2022-08-23T13:49:09.744493Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "M:\\Python_environments\\miniconda\\envs\\MLSS2\\Lib\\site-packages\\xgboost\\plotting.py:268: FutureWarning: The `num_trees` parameter is deprecated, use `tree_idx` instead. \n",
      "  warnings.warn(\n"
     ]
    },
    {
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       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2115.99\" y=\"-366.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_21_n_oI&lt;0.615400076</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;5 -->\n",
       "<g id=\"edge35\" class=\"edge\">\n",
       "<title>2&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M2115.99,-442.41C2115.99,-430.76 2115.99,-415.05 2115.99,-401.52\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"2119.49,-401.86 2115.99,-391.86 2112.49,-401.86 2119.49,-401.86\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2124.99\" y=\"-411.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g id=\"node37\" class=\"node\">\n",
       "<title>6</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2399.99\" cy=\"-372\" rx=\"98.95\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2399.99\" y=\"-366.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">chg_recv&lt;0.341733605</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;6 -->\n",
       "<g id=\"edge36\" class=\"edge\">\n",
       "<title>2&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M2169.07,-443.33C2217.68,-428.53 2289.56,-406.63 2340.18,-391.22\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"2340.97,-394.64 2349.51,-388.37 2338.93,-387.94 2340.97,-394.64\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2315.3\" y=\"-411.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 7 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>7</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"257.99\" cy=\"-283.5\" rx=\"124.54\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"257.99\" y=\"-278.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_31_n_oI&lt;&#45;0.276235491</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;7 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>3&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M502.37,-355.44C451.97,-340.81 376.45,-318.88 322.71,-303.29\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"323.97,-300.01 313.39,-300.58 322.01,-306.73 323.97,-300.01\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"442.68\" y=\"-322.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>8</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"555.99\" cy=\"-283.5\" rx=\"103.04\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"555.99\" y=\"-278.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">logtotasset&lt;&#45;1.59205961</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;8 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>3&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M555.99,-353.91C555.99,-342.26 555.99,-326.55 555.99,-313.02\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"559.49,-313.36 555.99,-303.36 552.49,-313.36 559.49,-313.36\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"587.86\" y=\"-322.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 9 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>9</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1173.99\" cy=\"-283.5\" rx=\"99.97\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1173.99\" y=\"-278.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">logtotasset&lt;1.30269408</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;9 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>4&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M1288.27,-354.12C1266.14,-340.63 1234.93,-321.62 1210.72,-306.88\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1212.81,-304.05 1202.45,-301.83 1209.16,-310.03 1212.81,-304.05\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1266.71\" y=\"-322.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>10</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1479.99\" cy=\"-283.5\" rx=\"124.54\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1479.99\" y=\"-278.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_21_n_oI&lt;&#45;0.143419459</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;10 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>4&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M1347.61,-354.32C1373.39,-340.72 1410.02,-321.4 1438.22,-306.53\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1439.63,-309.74 1446.85,-301.98 1436.37,-303.55 1439.63,-309.74\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1444.55\" y=\"-322.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 15 -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>15</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"78.99\" cy=\"-195\" rx=\"78.99\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"78.99\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=0.298255324</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;15 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>7&#45;&gt;15</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M223.48,-265.82C194.45,-251.8 152.83,-231.68 121.71,-216.64\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"123.48,-213.61 112.95,-212.41 120.43,-219.91 123.48,-213.61\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"193.52\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 16 -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>16</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"257.99\" cy=\"-195\" rx=\"82.06\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"257.99\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=&#45;0.212395415</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;16 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>7&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M257.99,-265.41C257.99,-253.76 257.99,-238.05 257.99,-224.52\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"261.49,-224.86 257.99,-214.86 254.49,-224.86 261.49,-224.86\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"289.86\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 17 -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>17</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"436.99\" cy=\"-195\" rx=\"78.99\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"436.99\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=0.147831559</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;17 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>8&#45;&gt;17</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M532.76,-265.62C514.54,-252.37 488.99,-233.8 468.86,-219.17\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"471.12,-216.49 460.98,-213.44 467.01,-222.15 471.12,-216.49\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"516.15\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 18 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>18</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"607.99\" cy=\"-195\" rx=\"74.38\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"607.99\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=1.22113252</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;18 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>8&#45;&gt;18</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M566.26,-265.41C573.6,-253.21 583.61,-236.55 591.99,-222.62\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"594.8,-224.73 596.96,-214.35 588.81,-221.12 594.8,-224.73\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"618.52\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 19 -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>19</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"795.99\" cy=\"-195\" rx=\"95.88\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"795.99\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">paralen_s&lt;1.00319266</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;19 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>9&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M1114.09,-268.79C1046.81,-253.4 937.71,-228.43 866.4,-212.11\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"867.3,-208.73 856.77,-209.91 865.74,-215.55 867.3,-208.73\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1027.85\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 20 -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>20</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1173.99\" cy=\"-195\" rx=\"124.54\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1173.99\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_11_n_oI&lt;&#45;0.295223117</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;20 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>9&#45;&gt;20</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M1173.99,-265.41C1173.99,-253.76 1173.99,-238.05 1173.99,-224.52\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1177.49,-224.86 1173.99,-214.86 1170.49,-224.86 1177.49,-224.86\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1205.86\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 21 -->\n",
       "<g id=\"node28\" class=\"node\">\n",
       "<title>21</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1479.99\" cy=\"-195\" rx=\"119.93\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1479.99\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_3_n_oI&lt;&#45;0.331051439</text>\n",
       "</g>\n",
       "<!-- 10&#45;&gt;21 -->\n",
       "<g id=\"edge27\" class=\"edge\">\n",
       "<title>10&#45;&gt;21</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M1479.99,-265.41C1479.99,-253.76 1479.99,-238.05 1479.99,-224.52\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1483.49,-224.86 1479.99,-214.86 1476.49,-224.86 1483.49,-224.86\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1488.99\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 22 -->\n",
       "<g id=\"node29\" class=\"node\">\n",
       "<title>22</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1760.99\" cy=\"-195\" rx=\"121.47\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1760.99\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_31_n_oI&lt;0.343781799</text>\n",
       "</g>\n",
       "<!-- 10&#45;&gt;22 -->\n",
       "<g id=\"edge28\" class=\"edge\">\n",
       "<title>10&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M1531.2,-266.74C1578.54,-252.16 1648.98,-230.48 1699.37,-214.97\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1700.15,-218.39 1708.68,-212.1 1698.09,-211.7 1700.15,-218.39\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1677.53\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 25 -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>25</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"321.99\" cy=\"-106.5\" rx=\"120.45\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"321.99\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">lm_negative_p&lt;0.950329721</text>\n",
       "</g>\n",
       "<!-- 19&#45;&gt;25 -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>19&#45;&gt;25</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M728.83,-181.74C644.49,-166.35 499.7,-139.93 407.53,-123.11\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"408.23,-119.68 397.76,-121.33 406.97,-126.57 408.23,-119.68\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"610.44\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 26 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>26</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"684.99\" cy=\"-106.5\" rx=\"119.93\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"684.99\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_4_n_oI&lt;&#45;0.127335146</text>\n",
       "</g>\n",
       "<!-- 19&#45;&gt;26 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>19&#45;&gt;26</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M774.32,-177.12C757.7,-164.16 734.53,-146.1 715.95,-131.63\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"718.21,-128.95 708.17,-125.57 713.91,-134.47 718.21,-128.95\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"782.31\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 27 -->\n",
       "<g id=\"node22\" class=\"node\">\n",
       "<title>27</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"957.99\" cy=\"-106.5\" rx=\"124.54\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"957.99\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_7_n_oI&lt;&#45;0.0221904907</text>\n",
       "</g>\n",
       "<!-- 20&#45;&gt;27 -->\n",
       "<g id=\"edge21\" class=\"edge\">\n",
       "<title>20&#45;&gt;27</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M1133.11,-177.63C1098,-163.57 1047.23,-143.24 1009.38,-128.08\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1011.06,-124.98 1000.48,-124.51 1008.46,-131.48 1011.06,-124.98\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1094.34\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 28 -->\n",
       "<g id=\"node23\" class=\"node\">\n",
       "<title>28</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1219.99\" cy=\"-106.5\" rx=\"119.93\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1219.99\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_2_n_oI&lt;&#45;0.474801213</text>\n",
       "</g>\n",
       "<!-- 20&#45;&gt;28 -->\n",
       "<g id=\"edge22\" class=\"edge\">\n",
       "<title>20&#45;&gt;28</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M1183.08,-176.91C1189.46,-164.9 1198.14,-148.59 1205.47,-134.8\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1208.45,-136.66 1210.05,-126.19 1202.27,-133.37 1208.45,-136.66\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1232.98\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 33 -->\n",
       "<g id=\"node18\" class=\"node\">\n",
       "<title>33</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"137.99\" cy=\"-18\" rx=\"82.06\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"137.99\" y=\"-12.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=&#45;0.225886837</text>\n",
       "</g>\n",
       "<!-- 25&#45;&gt;33 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>25&#45;&gt;33</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M286.51,-88.82C256.68,-74.8 213.89,-54.68 181.9,-39.64\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"183.41,-36.48 172.87,-35.4 180.43,-42.82 183.41,-36.48\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"255.47\" y=\"-57.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 34 -->\n",
       "<g id=\"node19\" class=\"node\">\n",
       "<title>34</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"321.99\" cy=\"-18\" rx=\"83.6\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"321.99\" y=\"-12.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=0.0339902081</text>\n",
       "</g>\n",
       "<!-- 25&#45;&gt;34 -->\n",
       "<g id=\"edge18\" class=\"edge\">\n",
       "<title>25&#45;&gt;34</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M321.99,-88.41C321.99,-76.76 321.99,-61.05 321.99,-47.52\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"325.49,-47.86 321.99,-37.86 318.49,-47.86 325.49,-47.86\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"353.86\" y=\"-57.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 35 -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>35</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"505.99\" cy=\"-18\" rx=\"82.06\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"505.99\" y=\"-12.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=&#45;0.127058014</text>\n",
       "</g>\n",
       "<!-- 26&#45;&gt;35 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>26&#45;&gt;35</title>\n",
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       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"550.78,-36.76 540.25,-35.56 547.73,-43.06 550.78,-36.76\"/>\n",
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     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To run this, you need to:\n",
    "#     1. Install the python graphviz package\n",
    "#     2. Install graphviz from https://graphviz.org/download/\n",
    "#         - MAKE SURE TO SELECT one of the \"add to path\" options during installation if on Windows!\n",
    "\n",
    "xgb.to_graphviz(model_xgb_logistic, num_trees=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "082af88f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "M:\\Python_environments\\miniconda\\envs\\MLSS2\\Lib\\site-packages\\xgboost\\plotting.py:268: FutureWarning: The `num_trees` parameter is deprecated, use `tree_idx` instead. \n",
      "  warnings.warn(\n"
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       "<title>2</title>\n",
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       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1533.45\" y=\"-455.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_22_n_oI&lt;&#45;0.266438395</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;2 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>0&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M1428.52,-531.32C1448.44,-518.06 1476.54,-499.37 1498.65,-484.66\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1500.38,-487.71 1506.76,-479.26 1496.5,-481.89 1500.38,-487.71\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1511.97\" y=\"-499.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 3 -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>3</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"931.45\" cy=\"-372\" rx=\"99.97\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"931.45\" y=\"-366.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">typetoken&lt;&#45;1.11252701</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;3 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>1&#45;&gt;3</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M1216.32,-444.63C1155.97,-429.46 1062.44,-405.94 999.16,-390.03\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1000.13,-386.66 989.57,-387.61 998.42,-393.45 1000.13,-386.66\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1143.27\" y=\"-411.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 4 -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>4</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1293.45\" cy=\"-372\" rx=\"102.53\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1293.45\" y=\"-366.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">sentlen_s&lt;&#45;0.355484098</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>1&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M1279.01,-442.41C1281.43,-430.76 1284.7,-415.05 1287.52,-401.52\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1290.92,-402.34 1289.53,-391.83 1284.07,-400.91 1290.92,-402.34\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1317.94\" y=\"-411.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 5 -->\n",
       "<g id=\"node34\" class=\"node\">\n",
       "<title>5</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1521.45\" cy=\"-372\" rx=\"83.6\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1521.45\" y=\"-366.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=0.0815696418</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;5 -->\n",
       "<g id=\"edge33\" class=\"edge\">\n",
       "<title>2&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M1531.08,-442.41C1529.47,-430.76 1527.29,-415.05 1525.41,-401.52\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1528.91,-401.27 1524.07,-391.85 1521.98,-402.24 1528.91,-401.27\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1537.53\" y=\"-411.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g id=\"node35\" class=\"node\">\n",
       "<title>6</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1697.45\" cy=\"-372\" rx=\"74.38\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1697.45\" y=\"-366.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=1.03576505</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;6 -->\n",
       "<g id=\"edge34\" class=\"edge\">\n",
       "<title>2&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M1565.46,-442.62C1591.78,-428.74 1629.21,-409 1657.49,-394.08\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1658.91,-397.29 1666.12,-389.53 1655.64,-391.1 1658.91,-397.29\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1662.02\" y=\"-411.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 7 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>7</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"292.45\" cy=\"-283.5\" rx=\"124.54\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"292.45\" y=\"-278.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_10_n_oI&lt;&#45;0.325805396</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;7 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>3&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M853.42,-360.44C737.48,-344.74 519.74,-315.27 392.32,-298.02\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"392.92,-294.57 382.54,-296.69 391.98,-301.5 392.92,-294.57\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"678.19\" y=\"-322.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>8</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"931.45\" cy=\"-283.5\" rx=\"77.97\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"931.45\" y=\"-278.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">cffin&lt;1.00708044</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;8 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>3&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M931.45,-353.91C931.45,-342.26 931.45,-326.55 931.45,-313.02\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"934.95,-313.36 931.45,-303.36 927.95,-313.36 934.95,-313.36\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"963.33\" y=\"-322.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 9 -->\n",
       "<g id=\"node26\" class=\"node\">\n",
       "<title>9</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1293.45\" cy=\"-283.5\" rx=\"124.54\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1293.45\" y=\"-278.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_23_n_oI&lt;&#45;0.734149456</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;9 -->\n",
       "<g id=\"edge25\" class=\"edge\">\n",
       "<title>4&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M1293.45,-353.91C1293.45,-342.26 1293.45,-326.55 1293.45,-313.02\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1296.95,-313.36 1293.45,-303.36 1289.95,-313.36 1296.95,-313.36\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1302.45\" y=\"-322.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node27\" class=\"node\">\n",
       "<title>10</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1610.45\" cy=\"-283.5\" rx=\"116.86\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1610.45\" y=\"-278.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_13_n_oI&lt;0.07167916</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;10 -->\n",
       "<g id=\"edge26\" class=\"edge\">\n",
       "<title>4&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M1347.23,-356.33C1401.41,-341.54 1485.15,-318.69 1543.58,-302.75\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1544.36,-306.16 1553.09,-300.15 1542.52,-299.41 1544.36,-306.16\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1512.22\" y=\"-322.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 11 -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>11</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"77.45\" cy=\"-195\" rx=\"77.45\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"77.45\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=&#45;0.23318924</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;11 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>7&#45;&gt;11</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M251.51,-266.03C215.54,-251.55 163.17,-230.49 125.29,-215.24\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"127,-212.16 116.41,-211.67 124.38,-218.65 127,-212.16\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"213.21\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 12 -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>12</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"292.45\" cy=\"-195\" rx=\"119.93\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"292.45\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_8_n_oI&lt;&#45;0.180090398</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;12 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>7&#45;&gt;12</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M292.45,-265.41C292.45,-253.76 292.45,-238.05 292.45,-224.52\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"295.95,-224.86 292.45,-214.86 288.95,-224.86 295.95,-224.86\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"324.33\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 13 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>13</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"742.45\" cy=\"-195\" rx=\"126.08\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"742.45\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_10_n_oI&lt;0.0253142789</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;13 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>8&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M897.66,-267.04C867.35,-253.16 822.59,-232.68 788.92,-217.27\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"790.78,-214.27 780.23,-213.29 787.86,-220.63 790.78,-214.27\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"862.88\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 14 -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>14</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1020.45\" cy=\"-195\" rx=\"118.91\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1020.45\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">processedsize&lt;&#45;0.125081301</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;14 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>8&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M948.61,-265.82C961.62,-253.18 979.71,-235.6 994.49,-221.24\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"996.92,-223.75 1001.65,-214.27 992.04,-218.73 996.92,-223.75\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1015.8\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 19 -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>19</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"108.45\" cy=\"-106.5\" rx=\"78.99\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"108.45\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=0.861382723</text>\n",
       "</g>\n",
       "<!-- 12&#45;&gt;19 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>12&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M256.98,-177.32C227.14,-163.3 184.36,-143.18 152.36,-128.14\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"153.87,-124.98 143.33,-123.9 150.89,-131.32 153.87,-124.98\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"225.93\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 20 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>20</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"292.45\" cy=\"-106.5\" rx=\"86.67\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"292.45\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=&#45;0.0159360357</text>\n",
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       "<!-- 12&#45;&gt;20 -->\n",
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       "<title>12&#45;&gt;20</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M292.45,-176.91C292.45,-165.26 292.45,-149.55 292.45,-136.02\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"295.95,-136.36 292.45,-126.36 288.95,-136.36 295.95,-136.36\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"324.33\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 21 -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>21</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"501.45\" cy=\"-106.5\" rx=\"104.07\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"501.45\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">lag_sdvol&lt;&#45;0.815557241</text>\n",
       "</g>\n",
       "<!-- 13&#45;&gt;21 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>13&#45;&gt;21</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M697.12,-177.73C657.11,-163.37 598.69,-142.4 556.1,-127.11\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"557.38,-123.85 546.79,-123.77 555.02,-130.44 557.38,-123.85\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"652.54\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 22 -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>22</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"742.45\" cy=\"-106.5\" rx=\"92.3\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"742.45\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">alltags&lt;&#45;0.441028595</text>\n",
       "</g>\n",
       "<!-- 13&#45;&gt;22 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>13&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M742.45,-176.91C742.45,-165.26 742.45,-149.55 742.45,-136.02\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"745.95,-136.36 742.45,-126.36 738.95,-136.36 745.95,-136.36\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"774.33\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
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       "<g id=\"node20\" class=\"node\">\n",
       "<title>23</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1023.45\" cy=\"-106.5\" rx=\"124.54\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1023.45\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_13_n_oI&lt;&#45;0.150196046</text>\n",
       "</g>\n",
       "<!-- 14&#45;&gt;23 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>14&#45;&gt;23</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M1021.05,-176.91C1021.45,-165.26 1022,-149.55 1022.46,-136.02\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1025.95,-136.48 1022.8,-126.36 1018.95,-136.24 1025.95,-136.48\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1031.22\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
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       "<g id=\"node21\" class=\"node\">\n",
       "<title>24</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1394.45\" cy=\"-106.5\" rx=\"105.6\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1394.45\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">lag_sdvol&lt;0.0178490281</text>\n",
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       "<!-- 14&#45;&gt;24 -->\n",
       "<g id=\"edge20\" class=\"edge\">\n",
       "<title>14&#45;&gt;24</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M1083.47,-179.42C1149.67,-164.11 1253.6,-140.08 1322.85,-124.06\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1323.23,-127.57 1332.18,-121.9 1321.65,-120.75 1323.23,-127.57\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1272.83\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
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       "<g id=\"node16\" class=\"node\">\n",
       "<title>27</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"307.45\" cy=\"-18\" rx=\"78.99\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"307.45\" y=\"-12.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=0.157308459</text>\n",
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       "<!-- 21&#45;&gt;27 -->\n",
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       "<title>21&#45;&gt;27</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M464.96,-89.23C433.03,-74.99 386.54,-54.26 352.34,-39.01\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"354.09,-35.96 343.53,-35.09 351.24,-42.36 354.09,-35.96\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"430.83\" y=\"-57.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
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       "<!-- 28 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>28</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"486.45\" cy=\"-18\" rx=\"82.06\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"486.45\" y=\"-12.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=&#45;0.205622897</text>\n",
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       "<!-- 21&#45;&gt;28 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>21&#45;&gt;28</title>\n",
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "xgb.to_graphviz(model_xgb_logistic, num_trees=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "03cb78f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:49:14.307950Z",
     "start_time": "2022-08-23T13:49:14.264941Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "M:\\Python_environments\\miniconda\\envs\\MLSS2\\Lib\\site-packages\\xgboost\\plotting.py:268: FutureWarning: The `num_trees` parameter is deprecated, use `tree_idx` instead. \n",
      "  warnings.warn(\n"
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       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"474.06\" y=\"-278.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_20_n_oI&lt;0.163843215</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;7 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>3&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M704.66,-355.24C657.16,-340.66 586.47,-318.98 535.89,-303.47\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"537.14,-300.19 526.55,-300.6 535.08,-306.88 537.14,-300.19\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"649.32\" y=\"-322.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>8</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"756.06\" cy=\"-283.5\" rx=\"98.95\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"756.06\" y=\"-278.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">chg_recv&lt;0.884817123</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;8 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>3&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M756.06,-353.91C756.06,-342.26 756.06,-326.55 756.06,-313.02\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"759.56,-313.36 756.06,-303.36 752.56,-313.36 759.56,-313.36\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"787.93\" y=\"-322.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 9 -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>9</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1610.06\" cy=\"-283.5\" rx=\"112.26\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1610.06\" y=\"-278.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_4_n_oI&lt;1.19269776</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;9 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>4&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M1683.88,-353.91C1670.27,-341.12 1651.46,-323.43 1636.23,-309.11\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1638.98,-306.89 1629.3,-302.59 1634.19,-311.99 1638.98,-306.89\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1673.3\" y=\"-322.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node21\" class=\"node\">\n",
       "<title>10</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1862.06\" cy=\"-283.5\" rx=\"121.47\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1862.06\" y=\"-278.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_11_n_oI&lt;0.520932615</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;10 -->\n",
       "<g id=\"edge20\" class=\"edge\">\n",
       "<title>4&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M1732.53,-354.53C1757.65,-340.95 1793.5,-321.57 1821.11,-306.64\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1822.42,-309.91 1829.55,-302.07 1819.09,-303.75 1822.42,-309.91\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1828.27\" y=\"-322.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 15 -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>15</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"273.06\" cy=\"-195\" rx=\"96.9\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"273.06\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">clindex&lt;0.0267500114</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;15 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>7&#45;&gt;15</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M435.78,-266.03C403.06,-251.94 355.83,-231.62 320.67,-216.49\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"322.14,-213.31 311.57,-212.57 319.37,-219.74 322.14,-213.31\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"400.56\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 16 -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>16</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"474.06\" cy=\"-195\" rx=\"78.99\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"474.06\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=0.536578476</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;16 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>7&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M474.06,-265.41C474.06,-253.76 474.06,-238.05 474.06,-224.52\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"477.56,-224.86 474.06,-214.86 470.56,-224.86 477.56,-224.86\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"505.93\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 17 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>17</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"668.06\" cy=\"-195\" rx=\"90.25\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"668.06\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">chg_inv&lt;1.06777072</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;17 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>8&#45;&gt;17</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M738.67,-265.41C725.78,-252.73 707.99,-235.25 693.5,-221.01\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"696.07,-218.63 686.49,-214.12 691.17,-223.62 696.07,-218.63\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"728.94\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 18 -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>18</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"879.06\" cy=\"-195\" rx=\"102.53\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.06\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">sentlen_s&lt;&#45;0.405534893</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;18 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>8&#45;&gt;18</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M780.07,-265.62C798.78,-252.46 824.97,-234.04 845.72,-219.45\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"847.71,-222.33 853.87,-213.71 843.68,-216.6 847.71,-222.33\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"860.45\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 25 -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>25</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"82.06\" cy=\"-106.5\" rx=\"82.06\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"82.06\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=&#45;0.198193908</text>\n",
       "</g>\n",
       "<!-- 15&#45;&gt;25 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>15&#45;&gt;25</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M237.58,-177.93C206.3,-163.76 160.56,-143.05 126.77,-127.75\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"128.64,-124.75 118.08,-123.81 125.75,-131.13 128.64,-124.75\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"203.67\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 26 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>26</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"273.06\" cy=\"-106.5\" rx=\"91.27\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"273.06\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=&#45;0.00197529607</text>\n",
       "</g>\n",
       "<!-- 15&#45;&gt;26 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>15&#45;&gt;26</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M273.06,-176.91C273.06,-165.26 273.06,-149.55 273.06,-136.02\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"276.56,-136.36 273.06,-126.36 269.56,-136.36 276.56,-136.36\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"304.93\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 27 -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>27</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"483.06\" cy=\"-106.5\" rx=\"101\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"483.06\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">headerlen&lt;0.527863503</text>\n",
       "</g>\n",
       "<!-- 17&#45;&gt;27 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>17&#45;&gt;27</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M633.7,-177.93C603.9,-164 560.57,-143.74 528.02,-128.52\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"529.78,-125.48 519.24,-124.42 526.81,-131.82 529.78,-125.48\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"601.13\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 28 -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>28</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"681.06\" cy=\"-106.5\" rx=\"78.99\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"681.06\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=0.282614231</text>\n",
       "</g>\n",
       "<!-- 17&#45;&gt;28 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>17&#45;&gt;28</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M670.63,-176.91C672.38,-165.26 674.74,-149.55 676.77,-136.02\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"680.2,-136.76 678.23,-126.35 673.28,-135.72 680.2,-136.76\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"707.6\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 29 -->\n",
       "<g id=\"node18\" class=\"node\">\n",
       "<title>29</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"857.06\" cy=\"-106.5\" rx=\"78.99\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"857.06\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=0.485874057</text>\n",
       "</g>\n",
       "<!-- 18&#45;&gt;29 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>18&#45;&gt;29</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M874.71,-176.91C871.72,-165.14 867.67,-149.23 864.21,-135.61\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"867.7,-135.15 861.85,-126.32 860.92,-136.87 867.7,-135.15\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"879.03\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 30 -->\n",
       "<g id=\"node19\" class=\"node\">\n",
       "<title>30</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1036.06\" cy=\"-106.5\" rx=\"82.06\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1036.06\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=&#45;0.194260001</text>\n",
       "</g>\n",
       "<!-- 18&#45;&gt;30 -->\n",
       "<g id=\"edge18\" class=\"edge\">\n",
       "<title>18&#45;&gt;30</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M909.33,-177.32C934.22,-163.61 969.67,-144.08 996.75,-129.16\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"998.28,-132.31 1005.35,-124.42 994.91,-126.18 998.28,-132.31\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1003.5\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 37 -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>37</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"393.06\" cy=\"-18\" rx=\"82.06\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"393.06\" y=\"-12.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=&#45;0.270925879</text>\n",
       "</g>\n",
       "<!-- 27&#45;&gt;37 -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>27&#45;&gt;37</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M465.28,-88.41C452.09,-75.73 433.9,-58.25 419.08,-44.01\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"421.53,-41.51 411.9,-37.1 416.68,-46.56 421.53,-41.51\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"455.12\" y=\"-57.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 38 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>38</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"572.06\" cy=\"-18\" rx=\"78.99\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"572.06\" y=\"-12.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=0.144144759</text>\n",
       "</g>\n",
       "<!-- 27&#45;&gt;38 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>27&#45;&gt;38</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M500.64,-88.41C513.77,-75.65 531.91,-58.03 546.61,-43.73\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"549,-46.29 553.73,-36.81 544.12,-41.27 549,-46.29\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.41\" y=\"-57.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 19 -->\n",
       "<g id=\"node22\" class=\"node\">\n",
       "<title>19</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1430.06\" cy=\"-195\" rx=\"97.93\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1430.06\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">chg_inv&lt;&#45;0.578680873</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;19 -->\n",
       "<g id=\"edge21\" class=\"edge\">\n",
       "<title>9&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M1575.78,-266.03C1546.91,-252.15 1505.44,-232.22 1474.11,-217.17\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1475.8,-214.1 1465.27,-212.92 1472.77,-220.41 1475.8,-214.1\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1545.18\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 20 -->\n",
       "<g id=\"node23\" class=\"node\">\n",
       "<title>20</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1625.06\" cy=\"-195\" rx=\"78.99\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1625.06\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">leaf=0.547501087</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;20 -->\n",
       "<g id=\"edge22\" class=\"edge\">\n",
       "<title>9&#45;&gt;20</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M1613.02,-265.41C1615.04,-253.76 1617.77,-238.05 1620.11,-224.52\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"1623.53,-225.29 1621.79,-214.84 1616.64,-224.1 1623.53,-225.29\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1650.78\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 21 -->\n",
       "<g id=\"node30\" class=\"node\">\n",
       "<title>21</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1851.06\" cy=\"-195\" rx=\"124.54\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1851.06\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_31_n_oI&lt;&#45;0.252071947</text>\n",
       "</g>\n",
       "<!-- 10&#45;&gt;21 -->\n",
       "<g id=\"edge29\" class=\"edge\">\n",
       "<title>10&#45;&gt;21</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M1859.89,-265.41C1858.4,-253.76 1856.41,-238.05 1854.69,-224.52\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1858.19,-224.33 1853.46,-214.85 1851.25,-225.21 1858.19,-224.33\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1866.55\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
       "</g>\n",
       "<!-- 22 -->\n",
       "<g id=\"node31\" class=\"node\">\n",
       "<title>22</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2118.06\" cy=\"-195\" rx=\"124.54\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2118.06\" y=\"-189.95\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Topic_19_n_oI&lt;&#45;0.322309375</text>\n",
       "</g>\n",
       "<!-- 10&#45;&gt;22 -->\n",
       "<g id=\"edge30\" class=\"edge\">\n",
       "<title>10&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M1909.31,-266.53C1951.76,-252.19 2014.22,-231.09 2059.75,-215.7\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"2060.86,-219.02 2069.22,-212.5 2058.62,-212.39 2060.86,-219.02\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2044.86\" y=\"-234.2\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">no, missing</text>\n",
       "</g>\n",
       "<!-- 31 -->\n",
       "<g id=\"node24\" class=\"node\">\n",
       "<title>31</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1221.06\" cy=\"-106.5\" rx=\"85.13\" ry=\"18\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1221.06\" y=\"-101.45\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">fog&lt;&#45;0.0757915825</text>\n",
       "</g>\n",
       "<!-- 19&#45;&gt;31 -->\n",
       "<g id=\"edge23\" class=\"edge\">\n",
       "<title>19&#45;&gt;31</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M1391.73,-178.13C1357.19,-163.84 1306.27,-142.77 1269.04,-127.36\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"1270.42,-124.14 1259.85,-123.55 1267.75,-130.61 1270.42,-124.14\"/>\n",
       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1353.28\" y=\"-145.7\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">yes</text>\n",
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       "<!-- 32 -->\n",
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      "text/plain": [
       "<graphviz.sources.Source at 0x188a8a13890>"
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     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.to_graphviz(model_xgb_logistic, num_trees=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5c8b2359",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:49:22.407950Z",
     "start_time": "2022-08-23T13:49:17.229951Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "M:\\Python_environments\\miniconda\\envs\\MLSS2\\Lib\\site-packages\\xgboost\\plotting.py:268: FutureWarning: The `num_trees` parameter is deprecated, use `tree_idx` instead. \n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# If you want to view every tree contained in your final model, the below code will dump a PNG file of each tree\n",
    "# into a \"trees/\" directory in the same folder as this file.\n",
    "num_trees = len(model_xgb_logistic.get_dump())\n",
    "for tree_index in range(0, num_trees):\n",
    "    dot = xgb.to_graphviz(model_xgb_logistic, num_trees=tree_index)\n",
    "    dot.format = 'png'\n",
    "    dot.render(\"xgb_trees/tree{}\".format(tree_index))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed31d83",
   "metadata": {},
   "source": [
    "### Optimizing parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "720b11b8",
   "metadata": {},
   "source": [
    "Here we will start with the same parameters as above.  We will then train a model based on iteratively optimizing various parameters:\n",
    "\n",
    "1. `max_depth` and `min_child_weight`\n",
    "2. `eta`\n",
    "3. `gamma`\n",
    "4. `subsample` and `colsample_bytree`\n",
    "5. Number of rounds\n",
    "\n",
    "The below example is reasonably rigorous for a properly trained model, but with some intuition applied to keep the search space smaller and to to keep execution time reasonable.\n",
    "\n",
    "To make it easier to follow, we will use the same cross-validation methods as before, leaning on Scikit-learn.  The XGBoost package comes with an interface to Scikit-learn, which is accessed via the `xgb.XGBClassifier()` function rather than the `xgb.train()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "502e1ea2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:49:22.512950Z",
     "start_time": "2022-08-23T13:49:22.498951Z"
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   },
   "outputs": [],
   "source": [
    "param = {\n",
    "    'booster': 'gbtree',             # default -- tree based\n",
    "    'nthread': 8,                    # number of threads to use for parallel processing\n",
    "    'objective': 'binary:logistic',  # binary, output probabilities\n",
    "    'eval_metric': 'auc',            # maximize ROC AUC\n",
    "    'eta': 0.3,                      # shrinkage; [0, 1], default 0.3\n",
    "    'max_depth': 6,                  # maximum depth of each tree; default 6\n",
    "    'gamma': 0.1,                    # set above 0 to prune trees, [0, inf], default 0\n",
    "    'min_child_weight': 1,           # higher leads to more pruning of tress, [0, inf], default 1\n",
    "    'subsample': 0.8,                # Randomly subsample rows if in (0, 1), default 1\n",
    "    'colsample_bytree': 0.8,         # Randomly subsample variables if in (0, 1), default 1\n",
    "    'random_state': 70\n",
    "}\n",
    "n_rounds = 20\n",
    "random_states= [231534]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f19c8f86",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:49:37.490194Z",
     "start_time": "2022-08-23T13:49:22.588950Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'colsample_bytree': 1.0, 'eta': 0.05, 'gamma': 0, 'max_depth': 4, 'min_child_weight': 1, 'subsample': 0.8} 0.6834784818705524\n"
     ]
    }
   ],
   "source": [
    "param_test = {\n",
    " 'max_depth': [3, 4, 5],\n",
    " 'min_child_weight': [1, 5, 10],\n",
    " 'subsample': [0.7, 0.8, 0.9],\n",
    " 'colsample_bytree': [0.8, 1.0],\n",
    " 'eta': [0.05, 0.08, 0.1],\n",
    " 'gamma': [0, 0.1, 0.3],\n",
    " }\n",
    "\n",
    "del param['max_depth']\n",
    "del param['min_child_weight']\n",
    "del param['subsample']\n",
    "del param['colsample_bytree']\n",
    "del param['eta']\n",
    "del param['gamma']\n",
    "\n",
    "cv = model_selection.StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=random_states[0])\n",
    "search1 = model_selection.GridSearchCV(xgb.XGBClassifier(**param, n_estimators=n_rounds), \n",
    "             param_grid = param_test, scoring='roc_auc', n_jobs=12, cv=cv)\n",
    "search1.fit(train_X_logistic,train_Y_logistic)\n",
    "print(search1.best_params_, search1.best_score_)\n",
    "\n",
    "param.update(search1.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f31e1fbc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:50:00.174675Z",
     "start_time": "2022-08-23T13:49:54.970586Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopping after 28 rounds\n"
     ]
    }
   ],
   "source": [
    "num_round = 200\n",
    "cv_results = xgb.cv(\n",
    "    param, dtrain, num_round, nfold=10, stratified=False,\n",
    "    early_stopping_rounds=50, as_pandas=False,\n",
    ")\n",
    "n_rounds = len(next(iter(cv_results.values())))\n",
    "print('Stopping after {} rounds'.format(n_rounds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "dc7948ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:50:00.505528Z",
     "start_time": "2022-08-23T13:50:00.265675Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
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       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
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       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
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       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-8\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=True, eta=0.05, eval_metric=&#x27;auc&#x27;,\n",
       "              feature_types=None, feature_weights=None, gamma=0,\n",
       "              grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=28, n_jobs=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier\">?<span>Documentation for XGBClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('base_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-base_score;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=base_score,-typing.Union%5Bfloat%2C%20typing.List%5Bfloat%5D%2C%20NoneType%5D\">\n",
       "            base_score\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-base_score;\">\n",
       "            base_score: typing.Union[float, typing.List[float], NoneType]<br><br>The initial prediction score of all instances, global bias.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('booster',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">booster</td>\n",
       "            <td class=\"value\">&#x27;gbtree&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('callbacks',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-callbacks;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=callbacks,-typing.Optional%5Btyping.List%5Bxgboost.callback.TrainingCallback%5D%5D\">\n",
       "            callbacks\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-callbacks;\">\n",
       "            callbacks: typing.Optional[typing.List[xgboost.callback.TrainingCallback]]<br><br>List of callback functions that are applied at end of each iteration.<br>It is possible to use predefined callbacks by using<br>:ref:`Callback API &lt;callback_api&gt;`.<br><br>.. note::<br><br>   States in callback are not preserved during training, which means callback<br>   objects can not be reused for multiple training sessions without<br>   reinitialization or deepcopy.<br><br>.. code-block:: python<br><br>    for params in parameters_grid:<br>        # be sure to (re)initialize the callbacks before each run<br>        callbacks = [xgb.callback.LearningRateScheduler(custom_rates)]<br>        reg = xgboost.XGBRegressor(**params, callbacks=callbacks)<br>        reg.fit(X, y)</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bylevel',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-colsample_bylevel;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=colsample_bylevel,-typing.Optional%5Bfloat%5D\">\n",
       "            colsample_bylevel\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-colsample_bylevel;\">\n",
       "            colsample_bylevel: typing.Optional[float]<br><br>Subsample ratio of columns for each level.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bynode',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-colsample_bynode;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=colsample_bynode,-typing.Optional%5Bfloat%5D\">\n",
       "            colsample_bynode\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-colsample_bynode;\">\n",
       "            colsample_bynode: typing.Optional[float]<br><br>Subsample ratio of columns for each split.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-colsample_bytree;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=colsample_bytree,-typing.Optional%5Bfloat%5D\">\n",
       "            colsample_bytree\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-colsample_bytree;\">\n",
       "            colsample_bytree: typing.Optional[float]<br><br>Subsample ratio of columns when constructing each tree.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('device',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-device;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=device,-typing.Optional%5Bstr%5D\">\n",
       "            device\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-device;\">\n",
       "            device: typing.Optional[str]<br><br>.. versionadded:: 2.0.0<br><br>Device ordinal, available options are `cpu`, `cuda`, and `gpu`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('early_stopping_rounds',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-early_stopping_rounds;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=early_stopping_rounds,-typing.Optional%5Bint%5D\">\n",
       "            early_stopping_rounds\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-early_stopping_rounds;\">\n",
       "            early_stopping_rounds: typing.Optional[int]<br><br>.. versionadded:: 1.6.0<br><br>- Activates early stopping. Validation metric needs to improve at least once in<br>  every **early_stopping_rounds** round(s) to continue training.  Requires at<br>  least one item in **eval_set** in :py:meth:`fit`.<br><br>- If early stopping occurs, the model will have two additional attributes:<br>  :py:attr:`best_score` and :py:attr:`best_iteration`. These are used by the<br>  :py:meth:`predict` and :py:meth:`apply` methods to determine the optimal<br>  number of trees during inference. If users want to access the full model<br>  (including trees built after early stopping), they can specify the<br>  `iteration_range` in these inference methods. In addition, other utilities<br>  like model plotting can also use the entire model.<br><br>- If you prefer to discard the trees after `best_iteration`, consider using the<br>  callback function :py:class:`xgboost.callback.EarlyStopping`.<br><br>- If there&#x27;s more than one item in **eval_set**, the last entry will be used for<br>  early stopping.  If there&#x27;s more than one metric in **eval_metric**, the last<br>  metric will be used for early stopping.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('enable_categorical',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-enable_categorical;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=enable_categorical,-bool\">\n",
       "            enable_categorical\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-enable_categorical;\">\n",
       "            enable_categorical: bool<br><br>See the same parameter of :py:class:`DMatrix` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('eval_metric',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-eval_metric;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=eval_metric,-typing.Union%5Bstr%2C%20typing.List%5Btyping.Union%5Bstr%2C%20typing.Callable%5D%5D%2C%20typing.Callable%2C%20NoneType%5D\">\n",
       "            eval_metric\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-eval_metric;\">\n",
       "            eval_metric: typing.Union[str, typing.List[typing.Union[str, typing.Callable]], typing.Callable, NoneType]<br><br>.. versionadded:: 1.6.0<br><br>Metric used for monitoring the training result and early stopping.  It can be a<br>string or list of strings as names of predefined metric in XGBoost (See<br>:doc:`/parameter`), one of the metrics in :py:mod:`sklearn.metrics`, or any<br>other user defined metric that looks like `sklearn.metrics`.<br><br>If custom objective is also provided, then custom metric should implement the<br>corresponding reverse link function.<br><br>Unlike the `scoring` parameter commonly used in scikit-learn, when a callable<br>object is provided, it&#x27;s assumed to be a cost function and by default XGBoost<br>will minimize the result during early stopping.<br><br>For advanced usage on Early stopping like directly choosing to maximize instead<br>of minimize, see :py:obj:`xgboost.callback.EarlyStopping`.<br><br>See :doc:`/tutorials/custom_metric_obj` and :ref:`custom-obj-metric` for more<br>information.<br><br>.. code-block:: python<br><br>    from sklearn.datasets import load_diabetes<br>    from sklearn.metrics import mean_absolute_error<br>    X, y = load_diabetes(return_X_y=True)<br>    reg = xgb.XGBRegressor(<br>        tree_method=&quot;hist&quot;,<br>        eval_metric=mean_absolute_error,<br>    )<br>    reg.fit(X, y, eval_set=[(X, y)])</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auc&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('feature_types',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_types;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=feature_types,-typing.Optional%5Btyping.Sequence%5Bstr%5D%5D\">\n",
       "            feature_types\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_types;\">\n",
       "            feature_types: typing.Optional[typing.Sequence[str]]<br><br>.. versionadded:: 1.7.0<br><br>Used for specifying feature types without constructing a dataframe. See<br>the :py:class:`DMatrix` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('feature_weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_weights;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=feature_weights,-Optional%5BArrayLike%5D\">\n",
       "            feature_weights\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_weights;\">\n",
       "            feature_weights: Optional[ArrayLike]<br><br>Weight for each feature, defines the probability of each feature being selected<br>when colsample is being used.  All values must be greater than 0, otherwise a<br>`ValueError` is thrown.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('gamma',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-gamma;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=gamma,-typing.Optional%5Bfloat%5D\">\n",
       "            gamma\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-gamma;\">\n",
       "            gamma: typing.Optional[float]<br><br>(min_split_loss) Minimum loss reduction required to make a further partition on<br>a leaf node of the tree.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('grow_policy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-grow_policy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=grow_policy,-typing.Optional%5Bstr%5D\">\n",
       "            grow_policy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-grow_policy;\">\n",
       "            grow_policy: typing.Optional[str]<br><br>Tree growing policy.<br><br>- depthwise: Favors splitting at nodes closest to the node,<br>- lossguide: Favors splitting at nodes with highest loss change.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('importance_type',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">importance_type</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('interaction_constraints',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-interaction_constraints;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=interaction_constraints,-typing.Union%5Bstr%2C%20typing.List%5Btyping.Tuple%5Bstr%5D%5D%2C%20NoneType%5D\">\n",
       "            interaction_constraints\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-interaction_constraints;\">\n",
       "            interaction_constraints: typing.Union[str, typing.List[typing.Tuple[str]], NoneType]<br><br>Constraints for interaction representing permitted interactions.  The<br>constraints must be specified in the form of a nested list, e.g. ``[[0, 1], [2,<br>3, 4]]``, where each inner list is a group of indices of features that are<br>allowed to interact with each other.  See :doc:`tutorial<br>&lt;/tutorials/feature_interaction_constraint&gt;` for more information</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-learning_rate;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=learning_rate,-typing.Optional%5Bfloat%5D\">\n",
       "            learning_rate\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-learning_rate;\">\n",
       "            learning_rate: typing.Optional[float]<br><br>Boosting learning rate (xgb&#x27;s &quot;eta&quot;)</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_bin',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_bin;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=max_bin,-typing.Optional%5Bint%5D\">\n",
       "            max_bin\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_bin;\">\n",
       "            max_bin: typing.Optional[int]<br><br>If using histogram-based algorithm, maximum number of bins per feature</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_cat_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_cat_threshold;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=max_cat_threshold,-typing.Optional%5Bint%5D\">\n",
       "            max_cat_threshold\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_cat_threshold;\">\n",
       "            max_cat_threshold: typing.Optional[int]<br><br>.. versionadded:: 1.7.0<br><br>.. note:: This parameter is experimental<br><br>Maximum number of categories considered for each split. Used only by<br>partition-based splits for preventing over-fitting. Also, `enable_categorical`<br>needs to be set to have categorical feature support. See :doc:`Categorical Data<br>&lt;/tutorials/categorical&gt;` and :ref:`cat-param` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_cat_to_onehot',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_cat_to_onehot;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=max_cat_to_onehot,-Optional%5Bint%5D\">\n",
       "            max_cat_to_onehot\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_cat_to_onehot;\">\n",
       "            max_cat_to_onehot: Optional[int]<br><br>.. versionadded:: 1.6.0<br><br>.. note:: This parameter is experimental<br><br>A threshold for deciding whether XGBoost should use one-hot encoding based split<br>for categorical data.  When number of categories is lesser than the threshold<br>then one-hot encoding is chosen, otherwise the categories will be partitioned<br>into children nodes. Also, `enable_categorical` needs to be set to have<br>categorical feature support. See :doc:`Categorical Data<br>&lt;/tutorials/categorical&gt;` and :ref:`cat-param` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_delta_step',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_delta_step;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=max_delta_step,-typing.Optional%5Bfloat%5D\">\n",
       "            max_delta_step\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_delta_step;\">\n",
       "            max_delta_step: typing.Optional[float]<br><br>Maximum delta step we allow each tree&#x27;s weight estimation to be.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_depth;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=max_depth,-%20typing.Optional%5Bint%5D\">\n",
       "            max_depth\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_depth;\">\n",
       "            max_depth:  typing.Optional[int]<br><br>Maximum tree depth for base learners.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">4</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_leaves',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_leaves;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=max_leaves,-typing.Optional%5Bint%5D\">\n",
       "            max_leaves\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_leaves;\">\n",
       "            max_leaves: typing.Optional[int]<br><br>Maximum number of leaves; 0 indicates no limit.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_child_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_child_weight;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=min_child_weight,-typing.Optional%5Bfloat%5D\">\n",
       "            min_child_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_child_weight;\">\n",
       "            min_child_weight: typing.Optional[float]<br><br>Minimum sum of instance weight(hessian) needed in a child.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('missing',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-missing;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=missing,-float\">\n",
       "            missing\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-missing;\">\n",
       "            missing: float<br><br>Value in the data which needs to be present as a missing value. Default to<br>:py:data:`numpy.nan`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('monotone_constraints',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-monotone_constraints;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=monotone_constraints,-typing.Union%5Btyping.Dict%5Bstr%2C%20int%5D%2C%20str%2C%20NoneType%5D\">\n",
       "            monotone_constraints\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-monotone_constraints;\">\n",
       "            monotone_constraints: typing.Union[typing.Dict[str, int], str, NoneType]<br><br>Constraint of variable monotonicity.  See :doc:`tutorial &lt;/tutorials/monotonic&gt;`<br>for more information.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('multi_strategy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-multi_strategy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=multi_strategy,-typing.Optional%5Bstr%5D\">\n",
       "            multi_strategy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-multi_strategy;\">\n",
       "            multi_strategy: typing.Optional[str]<br><br>.. versionadded:: 2.0.0<br><br>.. note:: This parameter is working-in-progress.<br><br>The strategy used for training multi-target models, including multi-target<br>regression and multi-class classification. See :doc:`/tutorials/multioutput` for<br>more information.<br><br>- ``one_output_per_tree``: One model for each target.<br>- ``multi_output_tree``:  Use multi-target trees.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_estimators;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=n_estimators,-Optional%5Bint%5D\">\n",
       "            n_estimators\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_estimators;\">\n",
       "            n_estimators: Optional[int]<br><br>Number of boosting rounds.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">28</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=n_jobs,-typing.Optional%5Bint%5D\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: typing.Optional[int]<br><br>Number of parallel threads used to run xgboost.  When used with other<br>Scikit-Learn algorithms like grid search, you may choose which algorithm to<br>parallelize and balance the threads.  Creating thread contention will<br>significantly slow down both algorithms.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('num_parallel_tree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">num_parallel_tree</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=random_state,-typing.Union%5Bnumpy.random.mtrand.RandomState%2C%20numpy.random._generator.Generator%2C%20int%2C%20NoneType%5D\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: typing.Union[numpy.random.mtrand.RandomState, numpy.random._generator.Generator, int, NoneType]<br><br>Random number seed.<br><br>.. note::<br><br>   Using gblinear booster with shotgun updater is nondeterministic as<br>   it uses Hogwild algorithm.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">70</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-reg_alpha;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=reg_alpha,-typing.Optional%5Bfloat%5D\">\n",
       "            reg_alpha\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-reg_alpha;\">\n",
       "            reg_alpha: typing.Optional[float]<br><br>L1 regularization term on weights (xgb&#x27;s alpha).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_lambda',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-reg_lambda;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=reg_lambda,-typing.Optional%5Bfloat%5D\">\n",
       "            reg_lambda\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-reg_lambda;\">\n",
       "            reg_lambda: typing.Optional[float]<br><br>L2 regularization term on weights (xgb&#x27;s lambda).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sampling_method',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-sampling_method;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=sampling_method,-typing.Optional%5Bstr%5D\">\n",
       "            sampling_method\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-sampling_method;\">\n",
       "            sampling_method: typing.Optional[str]<br><br>Sampling method. Used only by the GPU version of ``hist`` tree method.<br><br>- ``uniform``: Select random training instances uniformly.<br>- ``gradient_based``: Select random training instances with higher probability<br>    when the gradient and hessian are larger. (cf. CatBoost)</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">\n",
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       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=scale_pos_weight,-typing.Optional%5Bfloat%5D\">\n",
       "            scale_pos_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scale_pos_weight;\">\n",
       "            scale_pos_weight: typing.Optional[float]<br><br>Balancing of positive and negative weights.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-subsample;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=subsample,-typing.Optional%5Bfloat%5D\">\n",
       "            subsample\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-subsample;\">\n",
       "            subsample: typing.Optional[float]<br><br>Subsample ratio of the training instance.</span>\n",
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       "    </td>\n",
       "            <td class=\"value\">0.8</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tree_method',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tree_method;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=tree_method,-typing.Optional%5Bstr%5D\">\n",
       "            tree_method\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tree_method;\">\n",
       "            tree_method: typing.Optional[str]<br><br>Specify which tree method to use.  Default to auto.  If this parameter is set to<br>default, XGBoost will choose the most conservative option available.  It&#x27;s<br>recommended to study this option from the parameters document :doc:`tree method<br>&lt;/treemethod&gt;`</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">\n",
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       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=validate_parameters,-typing.Optional%5Bbool%5D\">\n",
       "            validate_parameters\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-validate_parameters;\">\n",
       "            validate_parameters: typing.Optional[bool]<br><br>Give warnings for unknown parameter.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
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       "            <td><i class=\"copy-paste-icon\"\n",
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       "            style=\"anchor-name: --doc-link-verbosity;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=verbosity,-typing.Optional%5Bint%5D\">\n",
       "            verbosity\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbosity;\">\n",
       "            verbosity: typing.Optional[int]<br><br>The degree of verbosity. Valid values are 0 (silent) - 3 (debug).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('nthread',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">nthread</td>\n",
       "            <td class=\"value\">8</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('eta',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">eta</td>\n",
       "            <td class=\"value\">0.05</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('objective',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-objective;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.4.0/python/python_api.html#xgboost.XGBClassifier#:~:text=objective,-typing.Union%5Bstr%2C%20xgboost.objective.Objective%2C%20xgboost.sklearn._SklObjWProto%2C%20typing.Callable%5B%5Btyping.Any%2C%20typing.Any%5D%2C%20typing.Tuple%5Bnumpy.ndarray%2C%20numpy.ndarray%5D%5D%2C%20NoneType%5D\">\n",
       "            objective\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-objective;\">\n",
       "            objective: typing.Union[str, xgboost.objective.Objective, xgboost.sklearn._SklObjWProto, typing.Callable[[typing.Any, typing.Any], typing.Tuple[numpy.ndarray, numpy.ndarray]], NoneType]<br><br>Specify the learning task and the corresponding learning objective or a custom<br>objective to be used.<br><br>For custom objective, see :doc:`/tutorials/custom_metric_obj` and<br>:ref:`custom-obj-metric` for more information, along with the end note for<br>function signatures.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;binary:logistic&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">classes_</a></td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">feature_importances_</a></td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float32](68,)</td>\n",
       "           <td>[0.02,0.02,0.01,...,0.02,0.  ,0.  ]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">intercept_</a></td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float32](1,)</td>\n",
       "           <td>[0.01]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">n_classes_</a></td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>2</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">n_features_in_</a></td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>68</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
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       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-8');</script></body>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=True, eta=0.05, eval_metric='auc',\n",
       "              feature_types=None, feature_weights=None, gamma=0,\n",
       "              grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=28, n_jobs=None, ...)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = xgb.XGBClassifier(**param, n_estimators=n_rounds)\n",
    "final.fit(train_X_logistic,train_Y_logistic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "99dedbec-9c11-4618-b365-5efea4d97ac5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x188ccedae90>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.RocCurveDisplay.from_estimator(final, test_X_logistic, test_Y_logistic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "87913c6e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-23T13:50:01.345475Z",
     "start_time": "2022-08-23T13:50:00.807477Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,16))\n",
    "xgb.plot_importance(final, ax=ax)\n",
    "locs, labels = plt.yticks()\n",
    "new_labels = []\n",
    "for i in locs:\n",
    "    new_labels.append(vars_logistic[int(labels[i].get_text()[1:])])\n",
    "_ = plt.yticks(locs, new_labels)  # The `_ = ` here captures the excessive matplotlib output and deletes it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a7a759bc-cfb3-4b0e-9416-fc1b3640c545",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../../Data/S2_models.pkl', 'wb') as f:\n",
    "    pickle.dump({'SVC': grid_svc, 'XGBoost': final}, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb7c95da",
   "metadata": {},
   "source": [
    "# Clustering\n",
    "\n",
    "## Kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "02407642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.positron.dataexplorer+json": {
       "comm_id": "e4d388bd-a002-4c63-854f-6981e664f54e",
       "shape": {
        "columns": 2,
        "rows": 11478
       },
       "source": "polars",
       "title": "polars",
       "version": 1
      },
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (11_478, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>industry</th><th>sic</th></tr><tr><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Wholesale Trade&quot;</td><td>5080</td></tr><tr><td>&quot;Wholesale Trade&quot;</td><td>5080</td></tr><tr><td>&quot;Manufacturing&quot;</td><td>3661</td></tr><tr><td>&quot;Manufacturing&quot;</td><td>3661</td></tr><tr><td>&quot;Manufacturing&quot;</td><td>2834</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Retail Trade&quot;</td><td>5531</td></tr><tr><td>&quot;Wholesale Trade&quot;</td><td>5040</td></tr><tr><td>&quot;Manufacturing&quot;</td><td>2835</td></tr><tr><td>&quot;Services&quot;</td><td>8051</td></tr><tr><td>&quot;Services&quot;</td><td>7370</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (11_478, 2)\n",
       "┌─────────────────┬──────┐\n",
       "│ industry        ┆ sic  │\n",
       "│ ---             ┆ ---  │\n",
       "│ str             ┆ i64  │\n",
       "╞═════════════════╪══════╡\n",
       "│ Wholesale Trade ┆ 5080 │\n",
       "│ Wholesale Trade ┆ 5080 │\n",
       "│ Manufacturing   ┆ 3661 │\n",
       "│ Manufacturing   ┆ 3661 │\n",
       "│ Manufacturing   ┆ 2834 │\n",
       "│ …               ┆ …    │\n",
       "│ Retail Trade    ┆ 5531 │\n",
       "│ Wholesale Trade ┆ 5040 │\n",
       "│ Manufacturing   ┆ 2835 │\n",
       "│ Services        ┆ 8051 │\n",
       "│ Services        ┆ 7370 │\n",
       "└─────────────────┴──────┘"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_names = ['Topic_' + str(i) + '_n_oI' for i in range(1, 32)]\n",
    "industry_from_sic = (\n",
    "    pl.when(pl.col('sic').is_between(0, 999)).then(pl.lit('Agriculture'))\n",
    "    .when(pl.col('sic').is_between(1000, 1499)).then(pl.lit('Mining'))\n",
    "    .when(pl.col('sic').is_between(1500, 1799)).then(pl.lit('Construction'))\n",
    "    .when(pl.col('sic').is_between(2000, 3999)).then(pl.lit('Manufacturing'))\n",
    "    .when(pl.col('sic').is_between(4000, 4999)).then(pl.lit('Utilities'))\n",
    "    .when(pl.col('sic').is_between(5000, 5199)).then(pl.lit('Wholesale Trade'))\n",
    "    .when(pl.col('sic').is_between(5200, 5999)).then(pl.lit('Retail Trade'))\n",
    "    .when(pl.col('sic').is_between(6000, 6799)).then(pl.lit('Finance'))\n",
    "    .when(pl.col('sic').is_between(7000, 8999)).then(pl.lit('Services'))\n",
    "    .when(pl.col('sic').is_between(9100, 9999)).then(pl.lit('Public Admin'))\n",
    "    .otherwise(pl.lit('Unknown'))\n",
    "    .alias('industry')\n",
    ")\n",
    "train = train.with_columns(industry_from_sic)\n",
    "\n",
    "train.select('industry', 'sic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "fbd5878f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Industries'}>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "umap_color(\n",
    "    train.select(topic_names).to_numpy(),\n",
    "    train.get_column('industry').to_numpy(),\n",
    "    title='Industries',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a5309147",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = cluster.KMeans(n_clusters=9, n_init='auto')\n",
    "kmeans = model.fit(train.select(topic_names).to_numpy())\n",
    "train = train.with_columns(pl.Series('cluster', kmeans.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "93d015f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'K-means clusters'}>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "umap_color(\n",
    "    train.select(topic_names).to_numpy(),\n",
    "    train.get_column('cluster').cast(pl.String).to_numpy(),\n",
    "    title='K-means clusters',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7912dbb5",
   "metadata": {},
   "source": [
    "## Optimizing kmeans\n",
    "\n",
    "The Gap statistic provides us a way to optimize the number of clusters for kmeans in a way that is unsupervised and statistically grounded.  It is simulation based though (bootstrap-ish), which means it will take a while to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "948c3155",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k=2 Not optimal.  To optimize: 0.13\n",
      "k=3 Not optimal.  To optimize: 0.09\n",
      "k=4 Not optimal.  To optimize: 0.09\n",
      "k=5 Not optimal.  To optimize: 0.08\n",
      "k=6 Not optimal.  To optimize: 0.04\n",
      "k=7 Not optimal.  To optimize: 0.07\n",
      "k=8 Not optimal.  To optimize: 0.07\n",
      "k=9 Not optimal.  To optimize: 0.05\n",
      "k=10 Not optimal.  To optimize: 0.05\n",
      "k=11 Not optimal.  To optimize: 0.04\n",
      "k=12 Not optimal.  To optimize: 0.03\n",
      "k=13 Not optimal.  To optimize: 0.04\n",
      "k=14 Not optimal.  To optimize: 0.00\n",
      "k=15 Not optimal.  To optimize: 0.06\n",
      "k=16 Not optimal.  To optimize: 0.03\n",
      "Optimal k found: 17\n",
      "-0.011332538640951958\n"
     ]
    }
   ],
   "source": [
    "iterations = 10\n",
    "ks = []\n",
    "gaps = []\n",
    "sks = []\n",
    "inertias = []\n",
    "\n",
    "k = 2\n",
    "init_k = k\n",
    "optimizing = True\n",
    "while optimizing:\n",
    "    model = cluster.KMeans(n_clusters=k, n_init='auto')\n",
    "    kmeans = model.fit(train.select(topic_names).to_numpy())\n",
    "    inertia_at_k = kmeans.inertia_\n",
    "    # run 50 iterations to determine s.d.\n",
    "    sim_inertias = []\n",
    "    for i in range(0,iterations):\n",
    "        model = cluster.KMeans(n_clusters=k, n_init='auto')\n",
    "        # Simulate on random data\n",
    "        kmeans = model.fit(np.random.rand(train.height, len(topic_names)))\n",
    "        sim_inertias.append(kmeans.inertia_)\n",
    "    l = np.mean(np.log(sim_inertias))\n",
    "    gap = l - np.log(inertia_at_k)\n",
    "    sk = (sum((1/iterations) * (np.log(sim_inertias) - l)**2)**(1/2)) * (1+1/iterations)**(1/2)\n",
    "    ks.append(k)\n",
    "    sks.append(sk)\n",
    "    gaps.append(gap)\n",
    "    inertias.append(inertia_at_k)\n",
    "    if k > init_k:\n",
    "        thresh = gap - gaps[-2] - sks[-2]\n",
    "        if thresh < 0:\n",
    "            print('Optimal k found: ' + str(k-1))\n",
    "            print(thresh)\n",
    "            break\n",
    "        else:\n",
    "            print('k={} Not optimal.  To optimize: {:.2f}'.format(k-1, thresh))\n",
    "            k += 1\n",
    "    else:\n",
    "        k += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d449c425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using optimal k = 17\n"
     ]
    }
   ],
   "source": [
    "print('Using optimal k = ' + str(k-1))\n",
    "model = cluster.KMeans(n_clusters=k-1, n_init='auto')\n",
    "kmeans = model.fit(train.select(topic_names).to_numpy())\n",
    "train = train.with_columns(pl.Series('cluster_opt', kmeans.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "1a4e4e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tables = [\n",
    "    train.filter(pl.col('cluster_opt') == cluster_id).select('industry').sample(n=10, seed=42)\n",
    "    for cluster_id in (1, 2, 3)\n",
    "]\n",
    "with open('../Results/S2_cluster_tables.pkl', 'wb') as f:\n",
    "    pickle.dump(tables, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f72a4fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Optimized K-means clusters'}>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "umap_color(\n",
    "    train.select(topic_names).to_numpy(),\n",
    "    train.get_column('cluster_opt').cast(pl.String).to_numpy(),\n",
    "    title='Optimized K-means clusters',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99efe8da",
   "metadata": {},
   "source": [
    "## KNN for multiclass prediction\n",
    "\n",
    "To set up this problem, we need to apply our industry classification to the testing data as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e878fdd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.with_columns(industry_from_sic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeb8051e",
   "metadata": {},
   "source": [
    "To set up a KNN model, we will use the `KNeighborsClassifier()` function from Scikit Learn.  The most important parameter is `n_neighbors`, which corresponds to the `k` in KNN -- how many neighbors to use for classification.  There are other parameters for the Scikit Learn implementation as well, which can be seen in the [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2e71ea98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
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       "}\n",
       "\n",
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       "\n",
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       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-9\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_neighbors',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_neighbors;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
       "            n_neighbors\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_neighbors;\">\n",
       "            n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-weights;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
       "            weights\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-weights;\">\n",
       "            weights: {&#x27;uniform&#x27;, &#x27;distance&#x27;}, callable or None, default=&#x27;uniform&#x27;<br><br>Weight function used in prediction.  Possible values:<br><br>- &#x27;uniform&#x27; : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- &#x27;distance&#x27; : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-algorithm;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
       "            algorithm\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-algorithm;\">\n",
       "            algorithm: {&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;}, default=&#x27;auto&#x27;<br><br>Algorithm used to compute the nearest neighbors:<br><br>- &#x27;ball_tree&#x27; will use :class:`BallTree`<br>- &#x27;kd_tree&#x27; will use :class:`KDTree`<br>- &#x27;brute&#x27; will use a brute-force search.<br>- &#x27;auto&#x27; will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('leaf_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-leaf_size;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
       "            leaf_size\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-leaf_size;\">\n",
       "            leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">30</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('p',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-p;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
       "            p\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-p;\">\n",
       "            p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-metric;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
       "            metric\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-metric;\">\n",
       "            metric: str or callable, default=&#x27;minkowski&#x27;<br><br>Metric to use for distance computation. Default is &quot;minkowski&quot;, which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br>&lt;https://docs.scipy.org/doc/scipy/reference/spatial.distance.html&gt;`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is &quot;precomputed&quot;, X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only &quot;nonzero&quot; elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy&#x27;s metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric_params',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-metric_params;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
       "            metric_params\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-metric_params;\">\n",
       "            metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary &lt;n_jobs&gt;`<br>for more details.<br>Doesn&#x27;t affect :meth:`fit` method.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=classes_,-array%20of%20shape%20%28n_classes%2C%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: array of shape (n_classes,)<br><br>Class labels known to the classifier</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](9,)</td>\n",
       "           <td>[&#x27;Agriculture&#x27;,&#x27;Construction&#x27;,&#x27;Manufacturing&#x27;,...,&#x27;Services&#x27;,&#x27;Utilities&#x27;,\n",
       " &#x27;Wholesale Trade&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-effective_metric_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=effective_metric_,-str%20or%20callble\">\n",
       "            effective_metric_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-effective_metric_;\">\n",
       "            effective_metric_: str or callble<br><br>The distance metric used. It will be same as the `metric` parameter<br>or a synonym of it, e.g. &#x27;euclidean&#x27; if the `metric` parameter set to<br>&#x27;minkowski&#x27; and `p` parameter set to 2.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">str</td>\n",
       "           <td>&#x27;eu...an&#x27;</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-effective_metric_params_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=effective_metric_params_,-dict\">\n",
       "            effective_metric_params_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-effective_metric_params_;\">\n",
       "            effective_metric_params_: dict<br><br>Additional keyword arguments for the metric function. For most metrics<br>will be same with `metric_params` parameter, but may also contain the<br>`p` parameter value if the `effective_metric_` attribute is set to<br>&#x27;minkowski&#x27;.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>31</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_fit_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_samples_fit_,-int\">\n",
       "            n_samples_fit_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_fit_;\">\n",
       "            n_samples_fit_: int<br><br>Number of samples in the fitted data.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>11478</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-outputs_2d_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=outputs_2d_,-bool\">\n",
       "            outputs_2d_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-outputs_2d_;\">\n",
       "            outputs_2d_: bool<br><br>False when `y`&#x27;s shape is (n_samples, ) or (n_samples, 1) during fit<br>otherwise True.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">bool</td>\n",
       "           <td>False</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "\n",
       "    const parent = element.parentElement;\n",
       "    if (!parent || !parent.nextElementSibling) {\n",
       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "/**\n",
       " * Copy the list of feature names formatted as a Python list.\n",
       " *\n",
       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
       " *   contain a `details` element and a table containing feature named.\n",
       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
       " */\n",
       "function copyFeatureNamesToClipboard(element) {\n",
       "    var detailsElem = element.closest('.features').querySelector('details');\n",
       "    var wasOpen = detailsElem.open;\n",
       "    detailsElem.open = true;\n",
       "    var content = element.closest('.features').querySelector('tbody')\n",
       "                  .innerText.trim();\n",
       "    if (!wasOpen) detailsElem.open = false;\n",
       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
       "    const originalStyle = element.style;\n",
       "    const copyMark = document.createElement('span');\n",
       "    copyMark.innerHTML = '✔';\n",
       "    copyMark.style.color = 'blue';\n",
       "    copyMark.style.fontSize = '1em';\n",
       "\n",
       "    navigator.clipboard.writeText(formattedText)\n",
       "        .then(() => {\n",
       "            element.style.display = 'none';\n",
       "            element.parentElement.appendChild(copyMark);\n",
       "\n",
       "            setTimeout(() => {\n",
       "                copyMark.remove();\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'orange';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-9');</script></body>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = neighbors.KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(\n",
    "    train.select(topic_names).to_numpy(),\n",
    "    train.get_column('industry').to_numpy(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eec87a0e",
   "metadata": {},
   "source": [
    "To see how well the algorithm works, we can make multiclass predictions using `.predict()`.  We can then pass the predictions to `sklearn.metrics.accuracy_score()` to get accuracy as a percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c1ebf8be",
   "metadata": {},
   "outputs": [],
   "source": [
    "in_pred = knn.predict(train.select(topic_names).to_numpy())\n",
    "out_pred = knn.predict(test.select(topic_names).to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2a9d63e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In sample: 0.9123540686530754,\n",
      "Out of sample: 0.8597236981934112\n"
     ]
    }
   ],
   "source": [
    "print('In sample: {},\\nOut of sample: {}'.format(\n",
    "    metrics.accuracy_score(train.get_column('industry').to_numpy(), in_pred),\n",
    "    metrics.accuracy_score(test.get_column('industry').to_numpy(), out_pred),\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73162d4",
   "metadata": {},
   "source": [
    "To optimize parameters for KNN, we can use a Grid Search.  It is fairly efficient to run, so we can do a full grid search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "09e52dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_opt = neighbors.KNeighborsClassifier(algorithm='auto')\n",
    "knn_param = {\n",
    "    'n_neighbors': [1,2,3,4,5,6,7,8,9,10],\n",
    "    'leaf_size': [10, 20, 30, 40],\n",
    "    'p': [1, 2],\n",
    "    'weights': ['uniform', 'distance'],\n",
    "}\n",
    "                   \n",
    "# with GridSearch\n",
    "grid_knn = model_selection.GridSearchCV(\n",
    "    estimator=knn_opt,\n",
    "    param_grid=knn_param,\n",
    "    scoring = 'accuracy',\n",
    "    n_jobs = -1,\n",
    "    cv = 5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "2cec905a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-10\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;leaf_size&#x27;: [10, 20, 30, 40],\n",
       "                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         &#x27;p&#x27;: [1, 2], &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('estimator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-estimator;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=estimator,-estimator%20object\">\n",
       "            estimator\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-estimator;\">\n",
       "            estimator: estimator object<br><br>This is assumed to implement the scikit-learn estimator interface.<br>Either estimator needs to provide a ``score`` function,<br>or ``scoring`` must be passed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">KNeighborsClassifier()</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('param_grid',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-param_grid;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=param_grid,-dict%20or%20list%20of%20dictionaries\">\n",
       "            param_grid\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-param_grid;\">\n",
       "            param_grid: dict or list of dictionaries<br><br>Dictionary with parameters names (`str`) as keys and lists of<br>parameter settings to try as values, or a list of such<br>dictionaries, in which case the grids spanned by each dictionary<br>in the list are explored. This enables searching over any sequence<br>of parameter settings.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">{&#x27;leaf_size&#x27;: [10, 20, ...], &#x27;n_neighbors&#x27;: [1, 2, ...], &#x27;p&#x27;: [1, 2], &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]}</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('scoring',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-scoring;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=scoring,-str%2C%20callable%2C%20list%2C%20tuple%20or%20dict%2C%20default%3DNone\">\n",
       "            scoring\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scoring;\">\n",
       "            scoring: str, callable, list, tuple or dict, default=None<br><br>Strategy to evaluate the performance of the cross-validated model on<br>the test set.<br><br>If `scoring` represents a single score, one can use:<br><br>- a single string (see :ref:`scoring_string_names`);<br>- a callable (see :ref:`scoring_callable`) that returns a single value;<br>- `None`, the `estimator`&#x27;s<br>  :ref:`default evaluation criterion &lt;scoring_api_overview&gt;` is used.<br><br>If `scoring` represents multiple scores, one can use:<br><br>- a list or tuple of unique strings;<br>- a callable returning a dictionary where the keys are the metric<br>  names and the values are the metric scores;<br>- a dictionary with metric names as keys and callables as values.<br><br>See :ref:`multimetric_grid_search` for an example.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;accuracy&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary &lt;n_jobs&gt;`<br>for more details.<br><br>.. versionchanged:: v0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cv',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-cv;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=cv,-int%2C%20cross-validation%20generator%20or%20an%20iterable%2C%20default%3DNone\">\n",
       "            cv\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-cv;\">\n",
       "            cv: int, cross-validation generator or an iterable, default=None<br><br>Determines the cross-validation splitting strategy.<br>Possible inputs for cv are:<br><br>- None, to use the default 5-fold cross validation,<br>- integer, to specify the number of folds in a `(Stratified)KFold`,<br>- :term:`CV splitter`,<br>- an iterable yielding (train, test) splits as arrays of indices.<br><br>For integer/None inputs, if the estimator is a classifier and ``y`` is<br>either binary or multiclass, :class:`StratifiedKFold` is used. In all<br>other cases, :class:`KFold` is used. These splitters are instantiated<br>with `shuffle=False` so the splits will be the same across calls.<br><br>Refer :ref:`User Guide &lt;cross_validation&gt;` for the various<br>cross-validation strategies that can be used here.<br><br>.. versionchanged:: 0.22<br>    ``cv`` default value if None changed from 3-fold to 5-fold.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('refit',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-refit;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=refit,-bool%2C%20str%2C%20or%20callable%2C%20default%3DTrue\">\n",
       "            refit\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-refit;\">\n",
       "            refit: bool, str, or callable, default=True<br><br>Refit an estimator using the best found parameters on the whole<br>dataset.<br><br>For multiple metric evaluation, this needs to be a `str` denoting the<br>scorer that would be used to find the best parameters for refitting<br>the estimator at the end.<br><br>Where there are considerations other than maximum score in<br>choosing a best estimator, ``refit`` can be set to a function which<br>returns the selected ``best_index_`` given ``cv_results_``. In that<br>case, the ``best_estimator_`` and ``best_params_`` will be set<br>according to the returned ``best_index_`` while the ``best_score_``<br>attribute will not be available.<br><br>The refitted estimator is made available at the ``best_estimator_``<br>attribute and permits using ``predict`` directly on this<br>``GridSearchCV`` instance.<br><br>Also for multiple metric evaluation, the attributes ``best_index_``,<br>``best_score_`` and ``best_params_`` will only be available if<br>``refit`` is set and all of them will be determined w.r.t this specific<br>scorer.<br><br>See ``scoring`` parameter to know more about multiple metric<br>evaluation.<br><br>See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`<br>to see how to design a custom selection strategy using a callable<br>via `refit`.<br><br>See :ref:`this example<br>&lt;sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py&gt;`<br>for an example of how to use ``refit=callable`` to balance model<br>complexity and cross-validated score.<br><br>.. versionchanged:: 0.20<br>    Support for callable added.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>Controls the verbosity of information printed during fitting, with higher<br>values yielding more detailed logging.<br><br>- 0 : no messages are printed;<br>- &gt;=1 : summary of the total number of fits;<br>- &gt;=2 : computation time for each fold and parameter candidate;<br>- &gt;=3 : fold indices and scores;<br>- &gt;=10 : parameter candidate indices and START messages before each fit.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-pre_dispatch;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=pre_dispatch,-int%2C%20or%20str%2C%20default%3D%272%2An_jobs%27\">\n",
       "            pre_dispatch\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-pre_dispatch;\">\n",
       "            pre_dispatch: int, or str, default=&#x27;2*n_jobs&#x27;<br><br>Controls the number of jobs that get dispatched during parallel<br>execution. Reducing this number can be useful to avoid an<br>explosion of memory consumption when more jobs get dispatched<br>than CPUs can process. This parameter can be:<br><br>- None, in which case all the jobs are immediately created and spawned. Use<br>  this for lightweight and fast-running jobs, to avoid delays due to on-demand<br>  spawning of the jobs<br>- An int, giving the exact number of total jobs that are spawned<br>- A str, giving an expression as a function of n_jobs, as in &#x27;2*n_jobs&#x27;</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('error_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-error_score;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=error_score,-%27raise%27%20or%20numeric%2C%20default%3Dnp.nan\">\n",
       "            error_score\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-error_score;\">\n",
       "            error_score: &#x27;raise&#x27; or numeric, default=np.nan<br><br>Value to assign to the score if an error occurs in estimator fitting.<br>If set to &#x27;raise&#x27;, the error is raised. If a numeric value is given,<br>FitFailedWarning is raised. This parameter does not affect the refit<br>step, which will always raise the error.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('return_train_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-return_train_score;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=return_train_score,-bool%2C%20default%3DFalse\">\n",
       "            return_train_score\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-return_train_score;\">\n",
       "            return_train_score: bool, default=False<br><br>If ``False``, the ``cv_results_`` attribute will not include training<br>scores.<br>Computing training scores is used to get insights on how different<br>parameter settings impact the overfitting/underfitting trade-off.<br>However computing the scores on the training set can be computationally<br>expensive and is not strictly required to select the parameters that<br>yield the best generalization performance.<br><br>.. versionadded:: 0.19<br><br>.. versionchanged:: 0.21<br>    Default value was changed from ``True`` to ``False``</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-best_estimator_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=best_estimator_,-estimator\">\n",
       "            best_estimator_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-best_estimator_;\">\n",
       "            best_estimator_: estimator<br><br>Estimator that was chosen by the search, i.e. estimator<br>which gave highest score (or smallest loss if specified)<br>on the left out data. Not available if ``refit=False``.<br><br>See ``refit`` parameter for more information on allowed values.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">KNeighborsClassifier</td>\n",
       "           <td>KNeighborsCla...ts=&#x27;distance&#x27;)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-best_index_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=best_index_,-int\">\n",
       "            best_index_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-best_index_;\">\n",
       "            best_index_: int<br><br>The index (of the ``cv_results_`` arrays) which corresponds to the best<br>candidate parameter setting.<br><br>The dict at ``search.cv_results_[&#x27;params&#x27;][search.best_index_]`` gives<br>the parameter setting for the best model, that gives the highest<br>mean score (``search.best_score_``).<br><br>For multi-metric evaluation, this is present only if ``refit`` is<br>specified.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int64</td>\n",
       "           <td>np.int64(37)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-best_params_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=best_params_,-dict\">\n",
       "            best_params_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-best_params_;\">\n",
       "            best_params_: dict<br><br>Parameter setting that gave the best results on the hold out data.<br><br>For multi-metric evaluation, this is present only if ``refit`` is<br>specified.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;le...ze&#x27;: 10, &#x27;n_...rs&#x27;: 10, &#x27;p&#x27;: 1, &#x27;weights&#x27;: &#x27;di...ce&#x27;}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-best_score_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=best_score_,-float\">\n",
       "            best_score_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-best_score_;\">\n",
       "            best_score_: float<br><br>Mean cross-validated score of the best_estimator<br><br>For multi-metric evaluation, this is present only if ``refit`` is<br>specified.<br><br>This attribute is not available if ``refit`` is a function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>0.7808</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes,)<br><br>The classes labels. This is present only if ``refit`` is specified and<br>the underlying estimator is a classifier.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](9,)</td>\n",
       "           <td>[&#x27;Agriculture&#x27;,&#x27;Construction&#x27;,&#x27;Manufacturing&#x27;,...,&#x27;Services&#x27;,&#x27;Utilities&#x27;,\n",
       " &#x27;Wholesale Trade&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-cv_results_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=cv_results_,-dict%20of%20numpy%20%28masked%29%20ndarrays\">\n",
       "            cv_results_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-cv_results_;\">\n",
       "            cv_results_: dict of numpy (masked) ndarrays<br><br>A dict with keys as column headers and values as columns, that can be<br>imported into a pandas ``DataFrame``.<br><br>For instance the below given table<br><br>+------------+-----------+------------+-----------------+---+---------+<br>|param_kernel|param_gamma|param_degree|split0_test_score|...|rank_t...|<br>+============+===========+============+=================+===+=========+<br>|  &#x27;poly&#x27;    |     --    |      2     |       0.80      |...|    2    |<br>+------------+-----------+------------+-----------------+---+---------+<br>|  &#x27;poly&#x27;    |     --    |      3     |       0.70      |...|    4    |<br>+------------+-----------+------------+-----------------+---+---------+<br>|  &#x27;rbf&#x27;     |     0.1   |     --     |       0.80      |...|    3    |<br>+------------+-----------+------------+-----------------+---+---------+<br>|  &#x27;rbf&#x27;     |     0.2   |     --     |       0.93      |...|    1    |<br>+------------+-----------+------------+-----------------+---+---------+<br><br>will be represented by a ``cv_results_`` dict of::<br><br>    {<br>    &#x27;param_kernel&#x27;: masked_array(data = [&#x27;poly&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;rbf&#x27;],<br>                                 mask = [False False False False]...)<br>    &#x27;param_gamma&#x27;: masked_array(data = [-- -- 0.1 0.2],<br>                                mask = [ True  True False False]...),<br>    &#x27;param_degree&#x27;: masked_array(data = [2.0 3.0 -- --],<br>                                 mask = [False False  True  True]...),<br>    &#x27;split0_test_score&#x27;  : [0.80, 0.70, 0.80, 0.93],<br>    &#x27;split1_test_score&#x27;  : [0.82, 0.50, 0.70, 0.78],<br>    &#x27;mean_test_score&#x27;    : [0.81, 0.60, 0.75, 0.85],<br>    &#x27;std_test_score&#x27;     : [0.01, 0.10, 0.05, 0.08],<br>    &#x27;rank_test_score&#x27;    : [2, 4, 3, 1],<br>    &#x27;split0_train_score&#x27; : [0.80, 0.92, 0.70, 0.93],<br>    &#x27;split1_train_score&#x27; : [0.82, 0.55, 0.70, 0.87],<br>    &#x27;mean_train_score&#x27;   : [0.81, 0.74, 0.70, 0.90],<br>    &#x27;std_train_score&#x27;    : [0.01, 0.19, 0.00, 0.03],<br>    &#x27;mean_fit_time&#x27;      : [0.73, 0.63, 0.43, 0.49],<br>    &#x27;std_fit_time&#x27;       : [0.01, 0.02, 0.01, 0.01],<br>    &#x27;mean_score_time&#x27;    : [0.01, 0.06, 0.04, 0.04],<br>    &#x27;std_score_time&#x27;     : [0.00, 0.00, 0.00, 0.01],<br>    &#x27;params&#x27;             : [{&#x27;kernel&#x27;: &#x27;poly&#x27;, &#x27;degree&#x27;: 2}, ...],<br>    }<br><br>For an example of visualization and interpretation of GridSearch results,<br>see :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py`.<br><br>NOTE<br><br>The key ``&#x27;params&#x27;`` is used to store a list of parameter<br>settings dicts for all the parameter candidates.<br><br>The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and<br>``std_score_time`` are all in seconds.<br><br>For multi-metric evaluation, the scores for all the scorers are<br>available in the ``cv_results_`` dict at the keys ending with that<br>scorer&#x27;s name (``&#x27;_&lt;scorer_name&gt;&#x27;``) instead of ``&#x27;_score&#x27;`` shown<br>above. (&#x27;split0_test_precision&#x27;, &#x27;mean_train_precision&#x27; etc.)</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;me...me&#x27;: array([0.6320..., 0.02922502]), &#x27;me...me&#x27;: array([0.9570..., 0.16166697]), &#x27;me...re&#x27;: array([0.7264..., 0.7586689 ]), &#x27;pa...ze&#x27;: masked_array(..._value=999999), ...}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-multimetric_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=multimetric_,-bool\">\n",
       "            multimetric_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-multimetric_;\">\n",
       "            multimetric_: bool<br><br>Whether or not the scorers compute several metrics.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">bool</td>\n",
       "           <td>False</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`. Only defined if<br>`best_estimator_` is defined (see the documentation for the `refit`<br>parameter for more details) and that `best_estimator_` exposes<br>`n_features_in_` when fit.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>31</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_splits_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_splits_,-int\">\n",
       "            n_splits_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_splits_;\">\n",
       "            n_splits_: int<br><br>The number of cross-validation splits (folds/iterations).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>5</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-refit_time_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=refit_time_,-float\">\n",
       "            refit_time_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-refit_time_;\">\n",
       "            refit_time_: float<br><br>Seconds used for refitting the best model on the whole dataset.<br><br>This is present only if ``refit`` is not False.<br><br>.. versionadded:: 0.20</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float</td>\n",
       "           <td>0.01489</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-scorer_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=scorer_,-function%20or%20a%20dict\">\n",
       "            scorer_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scorer_;\">\n",
       "            scorer_: function or a dict<br><br>Scorer function used on the held out data to choose the best<br>parameters for the model.<br><br>For multi-metric evaluation, this attribute holds the validated<br>``scoring`` dict which maps the scorer key to the scorer callable.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">_Scorer</td>\n",
       "           <td>make_scorer(a...hod=&#x27;predict&#x27;)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: KNeighborsClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>KNeighborsClassifier(leaf_size=10, n_neighbors=10, p=1, weights=&#x27;distance&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_neighbors',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_neighbors;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
       "            n_neighbors\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_neighbors;\">\n",
       "            n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-weights;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
       "            weights\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-weights;\">\n",
       "            weights: {&#x27;uniform&#x27;, &#x27;distance&#x27;}, callable or None, default=&#x27;uniform&#x27;<br><br>Weight function used in prediction.  Possible values:<br><br>- &#x27;uniform&#x27; : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- &#x27;distance&#x27; : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;distance&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('leaf_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-leaf_size;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
       "            leaf_size\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-leaf_size;\">\n",
       "            leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('p',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-p;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
       "            p\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-p;\">\n",
       "            p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-algorithm;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
       "            algorithm\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-algorithm;\">\n",
       "            algorithm: {&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;}, default=&#x27;auto&#x27;<br><br>Algorithm used to compute the nearest neighbors:<br><br>- &#x27;ball_tree&#x27; will use :class:`BallTree`<br>- &#x27;kd_tree&#x27; will use :class:`KDTree`<br>- &#x27;brute&#x27; will use a brute-force search.<br>- &#x27;auto&#x27; will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-metric;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
       "            metric\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-metric;\">\n",
       "            metric: str or callable, default=&#x27;minkowski&#x27;<br><br>Metric to use for distance computation. Default is &quot;minkowski&quot;, which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br>&lt;https://docs.scipy.org/doc/scipy/reference/spatial.distance.html&gt;`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is &quot;precomputed&quot;, X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only &quot;nonzero&quot; elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy&#x27;s metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric_params',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-metric_params;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
       "            metric_params\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-metric_params;\">\n",
       "            metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary &lt;n_jobs&gt;`<br>for more details.<br>Doesn&#x27;t affect :meth:`fit` method.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=classes_,-array%20of%20shape%20%28n_classes%2C%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: array of shape (n_classes,)<br><br>Class labels known to the classifier</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](9,)</td>\n",
       "           <td>[&#x27;Agriculture&#x27;,&#x27;Construction&#x27;,&#x27;Manufacturing&#x27;,...,&#x27;Services&#x27;,&#x27;Utilities&#x27;,\n",
       " &#x27;Wholesale Trade&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-effective_metric_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=effective_metric_,-str%20or%20callble\">\n",
       "            effective_metric_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-effective_metric_;\">\n",
       "            effective_metric_: str or callble<br><br>The distance metric used. It will be same as the `metric` parameter<br>or a synonym of it, e.g. &#x27;euclidean&#x27; if the `metric` parameter set to<br>&#x27;minkowski&#x27; and `p` parameter set to 2.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">str</td>\n",
       "           <td>&#x27;ma...an&#x27;</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-effective_metric_params_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=effective_metric_params_,-dict\">\n",
       "            effective_metric_params_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-effective_metric_params_;\">\n",
       "            effective_metric_params_: dict<br><br>Additional keyword arguments for the metric function. For most metrics<br>will be same with `metric_params` parameter, but may also contain the<br>`p` parameter value if the `effective_metric_` attribute is set to<br>&#x27;minkowski&#x27;.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>31</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_fit_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_samples_fit_,-int\">\n",
       "            n_samples_fit_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_fit_;\">\n",
       "            n_samples_fit_: int<br><br>Number of samples in the fitted data.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>11478</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-outputs_2d_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=outputs_2d_,-bool\">\n",
       "            outputs_2d_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-outputs_2d_;\">\n",
       "            outputs_2d_: bool<br><br>False when `y`&#x27;s shape is (n_samples, ) or (n_samples, 1) during fit<br>otherwise True.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">bool</td>\n",
       "           <td>False</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "\n",
       "    const parent = element.parentElement;\n",
       "    if (!parent || !parent.nextElementSibling) {\n",
       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "/**\n",
       " * Copy the list of feature names formatted as a Python list.\n",
       " *\n",
       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
       " *   contain a `details` element and a table containing feature named.\n",
       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
       " */\n",
       "function copyFeatureNamesToClipboard(element) {\n",
       "    var detailsElem = element.closest('.features').querySelector('details');\n",
       "    var wasOpen = detailsElem.open;\n",
       "    detailsElem.open = true;\n",
       "    var content = element.closest('.features').querySelector('tbody')\n",
       "                  .innerText.trim();\n",
       "    if (!wasOpen) detailsElem.open = false;\n",
       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
       "    const originalStyle = element.style;\n",
       "    const copyMark = document.createElement('span');\n",
       "    copyMark.innerHTML = '✔';\n",
       "    copyMark.style.color = 'blue';\n",
       "    copyMark.style.fontSize = '1em';\n",
       "\n",
       "    navigator.clipboard.writeText(formattedText)\n",
       "        .then(() => {\n",
       "            element.style.display = 'none';\n",
       "            element.parentElement.appendChild(copyMark);\n",
       "\n",
       "            setTimeout(() => {\n",
       "                copyMark.remove();\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'orange';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-10');</script></body>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(), n_jobs=-1,\n",
       "             param_grid={'leaf_size': [10, 20, 30, 40],\n",
       "                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'p': [1, 2], 'weights': ['uniform', 'distance']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_knn.fit(\n",
    "    train.select(topic_names).to_numpy(),\n",
    "    train.get_column('industry').to_numpy(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "26e4124f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'leaf_size': 10, 'n_neighbors': 10, 'p': 1, 'weights': 'distance'}\n"
     ]
    }
   ],
   "source": [
    "params = grid_knn.best_params_\n",
    "print(params) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "58b0078a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
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       "td.fitted-att-type {\n",
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       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
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       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
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       "    text-decoration: underline dashed;\n",
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       "\n",
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       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
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       "    text-align: left;\n",
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       "\n",
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       "    background: var(--sklearn-color-unfitted-level-0);\n",
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       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
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       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
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       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
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       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
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       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
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       "  border-radius: 0 0 .5em .5em;\n",
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       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
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       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-11\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(leaf_size=10, n_neighbors=10, p=1, weights=&#x27;distance&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_neighbors',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_neighbors;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
       "            n_neighbors\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_neighbors;\">\n",
       "            n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-weights;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
       "            weights\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-weights;\">\n",
       "            weights: {&#x27;uniform&#x27;, &#x27;distance&#x27;}, callable or None, default=&#x27;uniform&#x27;<br><br>Weight function used in prediction.  Possible values:<br><br>- &#x27;uniform&#x27; : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- &#x27;distance&#x27; : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;distance&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('leaf_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-leaf_size;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
       "            leaf_size\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-leaf_size;\">\n",
       "            leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('p',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-p;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
       "            p\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-p;\">\n",
       "            p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-algorithm;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
       "            algorithm\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-algorithm;\">\n",
       "            algorithm: {&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;}, default=&#x27;auto&#x27;<br><br>Algorithm used to compute the nearest neighbors:<br><br>- &#x27;ball_tree&#x27; will use :class:`BallTree`<br>- &#x27;kd_tree&#x27; will use :class:`KDTree`<br>- &#x27;brute&#x27; will use a brute-force search.<br>- &#x27;auto&#x27; will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-metric;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
       "            metric\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-metric;\">\n",
       "            metric: str or callable, default=&#x27;minkowski&#x27;<br><br>Metric to use for distance computation. Default is &quot;minkowski&quot;, which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br>&lt;https://docs.scipy.org/doc/scipy/reference/spatial.distance.html&gt;`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is &quot;precomputed&quot;, X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only &quot;nonzero&quot; elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy&#x27;s metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric_params',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-metric_params;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
       "            metric_params\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-metric_params;\">\n",
       "            metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary &lt;n_jobs&gt;`<br>for more details.<br>Doesn&#x27;t affect :meth:`fit` method.</span>\n",
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       "            <td class=\"value\">None</td>\n",
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       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
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       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=classes_,-array%20of%20shape%20%28n_classes%2C%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: array of shape (n_classes,)<br><br>Class labels known to the classifier</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](9,)</td>\n",
       "           <td>[&#x27;Agriculture&#x27;,&#x27;Construction&#x27;,&#x27;Manufacturing&#x27;,...,&#x27;Services&#x27;,&#x27;Utilities&#x27;,\n",
       " &#x27;Wholesale Trade&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-effective_metric_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=effective_metric_,-str%20or%20callble\">\n",
       "            effective_metric_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-effective_metric_;\">\n",
       "            effective_metric_: str or callble<br><br>The distance metric used. It will be same as the `metric` parameter<br>or a synonym of it, e.g. &#x27;euclidean&#x27; if the `metric` parameter set to<br>&#x27;minkowski&#x27; and `p` parameter set to 2.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">str</td>\n",
       "           <td>&#x27;ma...an&#x27;</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-effective_metric_params_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=effective_metric_params_,-dict\">\n",
       "            effective_metric_params_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-effective_metric_params_;\">\n",
       "            effective_metric_params_: dict<br><br>Additional keyword arguments for the metric function. For most metrics<br>will be same with `metric_params` parameter, but may also contain the<br>`p` parameter value if the `effective_metric_` attribute is set to<br>&#x27;minkowski&#x27;.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>31</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_fit_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_samples_fit_,-int\">\n",
       "            n_samples_fit_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_fit_;\">\n",
       "            n_samples_fit_: int<br><br>Number of samples in the fitted data.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>11478</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-outputs_2d_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=outputs_2d_,-bool\">\n",
       "            outputs_2d_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-outputs_2d_;\">\n",
       "            outputs_2d_: bool<br><br>False when `y`&#x27;s shape is (n_samples, ) or (n_samples, 1) during fit<br>otherwise True.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">bool</td>\n",
       "           <td>False</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
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      "text/plain": [
       "KNeighborsClassifier(leaf_size=10, n_neighbors=10, p=1, weights='distance')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn2 = neighbors.KNeighborsClassifier(**params)\n",
    "knn2.fit(\n",
    "    train.select(topic_names).to_numpy(),\n",
    "    train.get_column('industry').to_numpy(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "5276f315",
   "metadata": {},
   "outputs": [],
   "source": [
    "in_pred = knn2.predict(train.select(topic_names).to_numpy())\n",
    "out_pred = knn2.predict(test.select(topic_names).to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e81ec357",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In sample: 1.0,\n",
      "Out of sample: 0.8852284803400637\n"
     ]
    }
   ],
   "source": [
    "print('In sample: {},\\nOut of sample: {}'.format(\n",
    "    metrics.accuracy_score(train.get_column('industry').to_numpy(), in_pred),\n",
    "    metrics.accuracy_score(test.get_column('industry').to_numpy(), out_pred),\n",
    "))"
   ]
  }
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